Spaces:
Sleeping
Sleeping
atodorov284
commited on
Commit
·
3b0e766
1
Parent(s):
6547e96
Convert parameter space into a range rather than a grid.
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- air-quality-forecast/model_development.py +46 -3
- configs/hyperparameter_search_spaces.yaml +24 -3
- mlartifacts/149819317988706962/135e604974134ca4877227251e765174/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/135e604974134ca4877227251e765174/artifacts/model/conda.yaml +0 -15
- mlartifacts/149819317988706962/135e604974134ca4877227251e765174/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/estimator.html +0 -415
- mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/model/python_env.yaml +0 -7
- mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/estimator.html +0 -415
- mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/model/requirements.txt +0 -8
- mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/estimator.html +0 -415
- mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/conda.yaml +0 -15
- mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/python_env.yaml +0 -7
- mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/requirements.txt +0 -8
- mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/estimator.html +0 -415
- mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/conda.yaml +0 -15
- mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/python_env.yaml +0 -7
- mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/requirements.txt +0 -8
- mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/estimator.html +0 -415
- mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/conda.yaml +0 -15
- mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/python_env.yaml +0 -7
- mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/requirements.txt +0 -8
- mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/estimator.html +0 -415
- mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/MLmodel +0 -25
- mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/conda.yaml +0 -15
- mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/model.pkl +0 -3
- mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/python_env.yaml +0 -7
- mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/requirements.txt +0 -8
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/feature_importance_weight.json +0 -1
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/feature_importance_weight.png +0 -0
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/MLmodel +0 -25
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/conda.yaml +0 -15
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/model.xgb +0 -3
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/python_env.yaml +0 -7
- mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/requirements.txt +0 -8
- mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/feature_importance_weight.json +0 -1
- mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/feature_importance_weight.png +0 -0
- mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/MLmodel +0 -25
- mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/conda.yaml +0 -15
- mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/model.xgb +0 -3
- mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/python_env.yaml +0 -7
air-quality-forecast/model_development.py
CHANGED
|
@@ -6,6 +6,7 @@ from sklearn.base import BaseEstimator
|
|
| 6 |
from sklearn.metrics import mean_squared_error, root_mean_squared_error
|
| 7 |
from sklearn.model_selection import TimeSeriesSplit
|
| 8 |
from skopt import BayesSearchCV
|
|
|
|
| 9 |
from typing import Dict, Any
|
| 10 |
import socket
|
| 11 |
import pandas as pd
|
|
@@ -133,7 +134,7 @@ class RegressorTrainer:
|
|
| 133 |
mlflow.set_experiment(self._experiment_name)
|
| 134 |
mlflow.set_tracking_uri("http://localhost:5000/")
|
| 135 |
mlflow.enable_system_metrics_logging()
|
| 136 |
-
mlflow.autolog()
|
| 137 |
|
| 138 |
def _perform_search(self) -> None:
|
| 139 |
"""
|
|
@@ -169,8 +170,14 @@ class RegressorTrainer:
|
|
| 169 |
|
| 170 |
mlflow.log_params(self._bayes_search.best_params_)
|
| 171 |
|
| 172 |
-
|
| 173 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 174 |
def _evaluate_model(self) -> None:
|
| 175 |
"""
|
| 176 |
Evaluate the best model on the test data and log metrics.
|
|
@@ -231,6 +238,37 @@ class RegressorTrainer:
|
|
| 231 |
self._setup_mlflow()
|
| 232 |
self._optimize_and_evaluate()
|
| 233 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 234 |
|
| 235 |
def run_bayesian_optimization(
|
| 236 |
x_train: np.ndarray,
|
|
@@ -299,6 +337,11 @@ def train_all_models():
|
|
| 299 |
os.path.join(configs_data_path, "hyperparameter_search_spaces.yaml"), "r"
|
| 300 |
) as stream:
|
| 301 |
param_space_config = yaml.safe_load(stream)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 302 |
|
| 303 |
run_bayesian_optimization(
|
| 304 |
x_train,
|
|
|
|
| 6 |
from sklearn.metrics import mean_squared_error, root_mean_squared_error
|
| 7 |
from sklearn.model_selection import TimeSeriesSplit
|
| 8 |
from skopt import BayesSearchCV
|
| 9 |
+
from skopt.space import Real, Integer, Categorical
|
| 10 |
from typing import Dict, Any
|
| 11 |
import socket
|
| 12 |
import pandas as pd
|
|
|
|
| 134 |
mlflow.set_experiment(self._experiment_name)
|
| 135 |
mlflow.set_tracking_uri("http://localhost:5000/")
|
| 136 |
mlflow.enable_system_metrics_logging()
|
| 137 |
+
mlflow.autolog(silent=True)
|
| 138 |
|
| 139 |
def _perform_search(self) -> None:
|
| 140 |
"""
|
|
|
|
| 170 |
|
| 171 |
mlflow.log_params(self._bayes_search.best_params_)
|
| 172 |
|
| 173 |
+
|
| 174 |
+
best_regressor = self._bayes_search.best_estimator_
|
| 175 |
+
train_mse = mean_squared_error(
|
| 176 |
+
self._y_train, best_regressor.predict(self._x_train)
|
| 177 |
+
)
|
| 178 |
+
|
| 179 |
+
mlflow.log_metric("Correct Train MSE", train_mse)
|
| 180 |
+
|
| 181 |
def _evaluate_model(self) -> None:
|
| 182 |
"""
|
| 183 |
Evaluate the best model on the test data and log metrics.
|
|
|
|
| 238 |
self._setup_mlflow()
|
| 239 |
self._optimize_and_evaluate()
|
| 240 |
|
| 241 |
+
def convert_param_space(param_space: dict):
|
| 242 |
+
"""
|
| 243 |
+
Convert a parameter space dictionary to a format usable by skopt.
|
| 244 |
+
|
| 245 |
+
This function takes a dictionary where the keys are parameter names and the
|
| 246 |
+
values are lists of two values that represent the range of possible values
|
| 247 |
+
for that parameter. The function then converts these ranges to skopt
|
| 248 |
+
parameter objects and returns a new dictionary where the parameter names
|
| 249 |
+
are the same but the values are now skopt parameter objects.
|
| 250 |
+
|
| 251 |
+
Parameters:
|
| 252 |
+
param_space (dict): A dictionary with parameter names as keys and ranges
|
| 253 |
+
of possible values as values.
|
| 254 |
+
|
| 255 |
+
Returns:
|
| 256 |
+
dict: A dictionary with parameter names as keys and skopt parameter
|
| 257 |
+
objects as values.
|
| 258 |
+
"""
|
| 259 |
+
converted_space = {}
|
| 260 |
+
for param, values in param_space.items():
|
| 261 |
+
if isinstance(values[0], bool):
|
| 262 |
+
converted_space[param] = Categorical(values)
|
| 263 |
+
elif all(isinstance(v, int) for v in values):
|
| 264 |
+
converted_space[param] = Integer(values[0], values[1])
|
| 265 |
+
elif all(isinstance(v, float) for v in values):
|
| 266 |
+
converted_space[param] = Real(values[0], values[1])
|
| 267 |
+
elif all(isinstance(v, str) for v in values):
|
| 268 |
+
converted_space[param] = Categorical(values)
|
| 269 |
+
else:
|
| 270 |
+
raise ValueError(f"Unknown parameter type for {param}")
|
| 271 |
+
return converted_space
|
| 272 |
|
| 273 |
def run_bayesian_optimization(
|
| 274 |
x_train: np.ndarray,
|
|
|
|
| 337 |
os.path.join(configs_data_path, "hyperparameter_search_spaces.yaml"), "r"
|
| 338 |
) as stream:
|
| 339 |
param_space_config = yaml.safe_load(stream)
|
| 340 |
+
|
| 341 |
+
param_space_config["decision_tree"] = convert_param_space(param_space_config["decision_tree"])
|
| 342 |
+
print(param_space_config["decision_tree"])
|
| 343 |
+
param_space_config["xgboost"] = convert_param_space(param_space_config["xgboost"])
|
| 344 |
+
param_space_config["random_forest"] = convert_param_space(param_space_config["random_forest"])
|
| 345 |
|
| 346 |
run_bayesian_optimization(
|
| 347 |
x_train,
|
configs/hyperparameter_search_spaces.yaml
CHANGED
|
@@ -1,8 +1,29 @@
|
|
| 1 |
decision_tree:
|
| 2 |
-
max_depth: [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
|
| 4 |
random_forest:
|
| 5 |
-
max_depth: [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 6 |
|
| 7 |
xgboost:
|
| 8 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
decision_tree:
|
| 2 |
+
max_depth: [2, 100]
|
| 3 |
+
min_samples_split: [2, 20]
|
| 4 |
+
min_samples_leaf: [25, 35]
|
| 5 |
+
max_leaf_nodes: [20, 60]
|
| 6 |
+
ccp_alpha: [0.0, 0.1]
|
| 7 |
+
min_weight_fraction_leaf: [0.0, 0.5]
|
| 8 |
+
min_impurity_decrease: [0.0, 1.0]
|
| 9 |
+
max_features: ["sqrt", "log2", "auto"]
|
| 10 |
|
| 11 |
random_forest:
|
| 12 |
+
max_depth: [1, 50]
|
| 13 |
+
ccp_alpha: [0.0, 0.1]
|
| 14 |
+
n_estimators: [5, 500]
|
| 15 |
+
max_leaf_nodes: [1, 50]
|
| 16 |
+
min_samples_split: [1, 25]
|
| 17 |
+
min_samples_leaf: [1, 20]
|
| 18 |
+
max_features: [0.1, 1.0]
|
| 19 |
|
| 20 |
xgboost:
|
| 21 |
+
eta: [0.001, 0.1]
|
| 22 |
+
n_estimators: [100, 1000]
|
| 23 |
+
max_depth: [1, 20]
|
| 24 |
+
subsample: [0.5, 1.0]
|
| 25 |
+
colsample_bytree: [0.5, 1.0]
|
| 26 |
+
reg_lambda: [0.0, 2.0]
|
| 27 |
+
min_child_weight: [1, 15]
|
| 28 |
+
reg_alpha: [0.0, 2.0]
|
| 29 |
+
eval_metric: ["rmse"]
|
mlartifacts/149819317988706962/135e604974134ca4877227251e765174/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34769333
|
| 18 |
-
model_uuid: a4454c410eb04397aeb3487d1219327c
|
| 19 |
-
run_id: 135e604974134ca4877227251e765174
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-29 22:07:51.285707'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/135e604974134ca4877227251e765174/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- cloudpickle==3.0.0
|
| 9 |
-
- numpy==1.26.2
|
| 10 |
-
- pandas==2.2.2
|
| 11 |
-
- psutil==5.9.4
|
| 12 |
-
- scikit-learn==1.5.2
|
| 13 |
-
- scipy==1.11.4
|
| 14 |
-
- typing==3.7.4.3
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/135e604974134ca4877227251e765174/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:6b51c46f274e74ab79fd748cbe4fa01c5216d66f347bd96f71cfedf31015152f
|
| 3 |
-
size 34769333
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/estimator.html
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<!DOCTYPE html>
|
| 3 |
-
<html lang="en">
|
| 4 |
-
<head>
|
| 5 |
-
<meta charset="UTF-8"/>
|
| 6 |
-
</head>
|
| 7 |
-
<body>
|
| 8 |
-
<style>#sk-container-id-2 {
|
| 9 |
-
/* Definition of color scheme common for light and dark mode */
|
| 10 |
-
--sklearn-color-text: black;
|
| 11 |
-
--sklearn-color-line: gray;
|
| 12 |
-
/* Definition of color scheme for unfitted estimators */
|
| 13 |
-
--sklearn-color-unfitted-level-0: #fff5e6;
|
| 14 |
-
--sklearn-color-unfitted-level-1: #f6e4d2;
|
| 15 |
-
--sklearn-color-unfitted-level-2: #ffe0b3;
|
| 16 |
-
--sklearn-color-unfitted-level-3: chocolate;
|
| 17 |
-
/* Definition of color scheme for fitted estimators */
|
| 18 |
-
--sklearn-color-fitted-level-0: #f0f8ff;
|
| 19 |
-
--sklearn-color-fitted-level-1: #d4ebff;
|
| 20 |
-
--sklearn-color-fitted-level-2: #b3dbfd;
|
| 21 |
-
--sklearn-color-fitted-level-3: cornflowerblue;
|
| 22 |
-
|
| 23 |
-
/* Specific color for light theme */
|
| 24 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 25 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
|
| 26 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 27 |
-
--sklearn-color-icon: #696969;
|
| 28 |
-
|
| 29 |
-
@media (prefers-color-scheme: dark) {
|
| 30 |
-
/* Redefinition of color scheme for dark theme */
|
| 31 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 32 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
|
| 33 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 34 |
-
--sklearn-color-icon: #878787;
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
#sk-container-id-2 {
|
| 39 |
-
color: var(--sklearn-color-text);
|
| 40 |
-
}
|
| 41 |
-
|
| 42 |
-
#sk-container-id-2 pre {
|
| 43 |
-
padding: 0;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
#sk-container-id-2 input.sk-hidden--visually {
|
| 47 |
-
border: 0;
|
| 48 |
-
clip: rect(1px 1px 1px 1px);
|
| 49 |
-
clip: rect(1px, 1px, 1px, 1px);
|
| 50 |
-
height: 1px;
|
| 51 |
-
margin: -1px;
|
| 52 |
-
overflow: hidden;
|
| 53 |
-
padding: 0;
|
| 54 |
-
position: absolute;
|
| 55 |
-
width: 1px;
|
| 56 |
-
}
|
| 57 |
-
|
| 58 |
-
#sk-container-id-2 div.sk-dashed-wrapped {
|
| 59 |
-
border: 1px dashed var(--sklearn-color-line);
|
| 60 |
-
margin: 0 0.4em 0.5em 0.4em;
|
| 61 |
-
box-sizing: border-box;
|
| 62 |
-
padding-bottom: 0.4em;
|
| 63 |
-
background-color: var(--sklearn-color-background);
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
#sk-container-id-2 div.sk-container {
|
| 67 |
-
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
| 68 |
-
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
| 69 |
-
so we also need the `!important` here to be able to override the
|
| 70 |
-
default hidden behavior on the sphinx rendered scikit-learn.org.
|
| 71 |
-
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
| 72 |
-
display: inline-block !important;
|
| 73 |
-
position: relative;
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
#sk-container-id-2 div.sk-text-repr-fallback {
|
| 77 |
-
display: none;
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
div.sk-parallel-item,
|
| 81 |
-
div.sk-serial,
|
| 82 |
-
div.sk-item {
|
| 83 |
-
/* draw centered vertical line to link estimators */
|
| 84 |
-
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
| 85 |
-
background-size: 2px 100%;
|
| 86 |
-
background-repeat: no-repeat;
|
| 87 |
-
background-position: center center;
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
/* Parallel-specific style estimator block */
|
| 91 |
-
|
| 92 |
-
#sk-container-id-2 div.sk-parallel-item::after {
|
| 93 |
-
content: "";
|
| 94 |
-
width: 100%;
|
| 95 |
-
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
| 96 |
-
flex-grow: 1;
|
| 97 |
-
}
|
| 98 |
-
|
| 99 |
-
#sk-container-id-2 div.sk-parallel {
|
| 100 |
-
display: flex;
|
| 101 |
-
align-items: stretch;
|
| 102 |
-
justify-content: center;
|
| 103 |
-
background-color: var(--sklearn-color-background);
|
| 104 |
-
position: relative;
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
#sk-container-id-2 div.sk-parallel-item {
|
| 108 |
-
display: flex;
|
| 109 |
-
flex-direction: column;
|
| 110 |
-
}
|
| 111 |
-
|
| 112 |
-
#sk-container-id-2 div.sk-parallel-item:first-child::after {
|
| 113 |
-
align-self: flex-end;
|
| 114 |
-
width: 50%;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
#sk-container-id-2 div.sk-parallel-item:last-child::after {
|
| 118 |
-
align-self: flex-start;
|
| 119 |
-
width: 50%;
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
#sk-container-id-2 div.sk-parallel-item:only-child::after {
|
| 123 |
-
width: 0;
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
/* Serial-specific style estimator block */
|
| 127 |
-
|
| 128 |
-
#sk-container-id-2 div.sk-serial {
|
| 129 |
-
display: flex;
|
| 130 |
-
flex-direction: column;
|
| 131 |
-
align-items: center;
|
| 132 |
-
background-color: var(--sklearn-color-background);
|
| 133 |
-
padding-right: 1em;
|
| 134 |
-
padding-left: 1em;
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 139 |
-
clickable and can be expanded/collapsed.
|
| 140 |
-
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 141 |
-
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 142 |
-
*/
|
| 143 |
-
|
| 144 |
-
/* Pipeline and ColumnTransformer style (default) */
|
| 145 |
-
|
| 146 |
-
#sk-container-id-2 div.sk-toggleable {
|
| 147 |
-
/* Default theme specific background. It is overwritten whether we have a
|
| 148 |
-
specific estimator or a Pipeline/ColumnTransformer */
|
| 149 |
-
background-color: var(--sklearn-color-background);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
/* Toggleable label */
|
| 153 |
-
#sk-container-id-2 label.sk-toggleable__label {
|
| 154 |
-
cursor: pointer;
|
| 155 |
-
display: block;
|
| 156 |
-
width: 100%;
|
| 157 |
-
margin-bottom: 0;
|
| 158 |
-
padding: 0.5em;
|
| 159 |
-
box-sizing: border-box;
|
| 160 |
-
text-align: center;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:before {
|
| 164 |
-
/* Arrow on the left of the label */
|
| 165 |
-
content: "▸";
|
| 166 |
-
float: left;
|
| 167 |
-
margin-right: 0.25em;
|
| 168 |
-
color: var(--sklearn-color-icon);
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
|
| 172 |
-
color: var(--sklearn-color-text);
|
| 173 |
-
}
|
| 174 |
-
|
| 175 |
-
/* Toggleable content - dropdown */
|
| 176 |
-
|
| 177 |
-
#sk-container-id-2 div.sk-toggleable__content {
|
| 178 |
-
max-height: 0;
|
| 179 |
-
max-width: 0;
|
| 180 |
-
overflow: hidden;
|
| 181 |
-
text-align: left;
|
| 182 |
-
/* unfitted */
|
| 183 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 184 |
-
}
|
| 185 |
-
|
| 186 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted {
|
| 187 |
-
/* fitted */
|
| 188 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 189 |
-
}
|
| 190 |
-
|
| 191 |
-
#sk-container-id-2 div.sk-toggleable__content pre {
|
| 192 |
-
margin: 0.2em;
|
| 193 |
-
border-radius: 0.25em;
|
| 194 |
-
color: var(--sklearn-color-text);
|
| 195 |
-
/* unfitted */
|
| 196 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted pre {
|
| 200 |
-
/* unfitted */
|
| 201 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 202 |
-
}
|
| 203 |
-
|
| 204 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
| 205 |
-
/* Expand drop-down */
|
| 206 |
-
max-height: 200px;
|
| 207 |
-
max-width: 100%;
|
| 208 |
-
overflow: auto;
|
| 209 |
-
}
|
| 210 |
-
|
| 211 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
| 212 |
-
content: "▾";
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
/* Pipeline/ColumnTransformer-specific style */
|
| 216 |
-
|
| 217 |
-
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 218 |
-
color: var(--sklearn-color-text);
|
| 219 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 223 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
/* Estimator-specific style */
|
| 227 |
-
|
| 228 |
-
/* Colorize estimator box */
|
| 229 |
-
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 230 |
-
/* unfitted */
|
| 231 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 232 |
-
}
|
| 233 |
-
|
| 234 |
-
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 235 |
-
/* fitted */
|
| 236 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
|
| 240 |
-
#sk-container-id-2 div.sk-label label {
|
| 241 |
-
/* The background is the default theme color */
|
| 242 |
-
color: var(--sklearn-color-text-on-default-background);
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
/* On hover, darken the color of the background */
|
| 246 |
-
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
|
| 247 |
-
color: var(--sklearn-color-text);
|
| 248 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
/* Label box, darken color on hover, fitted */
|
| 252 |
-
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
| 253 |
-
color: var(--sklearn-color-text);
|
| 254 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 255 |
-
}
|
| 256 |
-
|
| 257 |
-
/* Estimator label */
|
| 258 |
-
|
| 259 |
-
#sk-container-id-2 div.sk-label label {
|
| 260 |
-
font-family: monospace;
|
| 261 |
-
font-weight: bold;
|
| 262 |
-
display: inline-block;
|
| 263 |
-
line-height: 1.2em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
#sk-container-id-2 div.sk-label-container {
|
| 267 |
-
text-align: center;
|
| 268 |
-
}
|
| 269 |
-
|
| 270 |
-
/* Estimator-specific */
|
| 271 |
-
#sk-container-id-2 div.sk-estimator {
|
| 272 |
-
font-family: monospace;
|
| 273 |
-
border: 1px dotted var(--sklearn-color-border-box);
|
| 274 |
-
border-radius: 0.25em;
|
| 275 |
-
box-sizing: border-box;
|
| 276 |
-
margin-bottom: 0.5em;
|
| 277 |
-
/* unfitted */
|
| 278 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
#sk-container-id-2 div.sk-estimator.fitted {
|
| 282 |
-
/* fitted */
|
| 283 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 284 |
-
}
|
| 285 |
-
|
| 286 |
-
/* on hover */
|
| 287 |
-
#sk-container-id-2 div.sk-estimator:hover {
|
| 288 |
-
/* unfitted */
|
| 289 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 290 |
-
}
|
| 291 |
-
|
| 292 |
-
#sk-container-id-2 div.sk-estimator.fitted:hover {
|
| 293 |
-
/* fitted */
|
| 294 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 295 |
-
}
|
| 296 |
-
|
| 297 |
-
/* Specification for estimator info (e.g. "i" and "?") */
|
| 298 |
-
|
| 299 |
-
/* Common style for "i" and "?" */
|
| 300 |
-
|
| 301 |
-
.sk-estimator-doc-link,
|
| 302 |
-
a:link.sk-estimator-doc-link,
|
| 303 |
-
a:visited.sk-estimator-doc-link {
|
| 304 |
-
float: right;
|
| 305 |
-
font-size: smaller;
|
| 306 |
-
line-height: 1em;
|
| 307 |
-
font-family: monospace;
|
| 308 |
-
background-color: var(--sklearn-color-background);
|
| 309 |
-
border-radius: 1em;
|
| 310 |
-
height: 1em;
|
| 311 |
-
width: 1em;
|
| 312 |
-
text-decoration: none !important;
|
| 313 |
-
margin-left: 1ex;
|
| 314 |
-
/* unfitted */
|
| 315 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 316 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
.sk-estimator-doc-link.fitted,
|
| 320 |
-
a:link.sk-estimator-doc-link.fitted,
|
| 321 |
-
a:visited.sk-estimator-doc-link.fitted {
|
| 322 |
-
/* fitted */
|
| 323 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 324 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
/* On hover */
|
| 328 |
-
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 329 |
-
.sk-estimator-doc-link:hover,
|
| 330 |
-
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 331 |
-
.sk-estimator-doc-link:hover {
|
| 332 |
-
/* unfitted */
|
| 333 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 334 |
-
color: var(--sklearn-color-background);
|
| 335 |
-
text-decoration: none;
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 339 |
-
.sk-estimator-doc-link.fitted:hover,
|
| 340 |
-
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 341 |
-
.sk-estimator-doc-link.fitted:hover {
|
| 342 |
-
/* fitted */
|
| 343 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 344 |
-
color: var(--sklearn-color-background);
|
| 345 |
-
text-decoration: none;
|
| 346 |
-
}
|
| 347 |
-
|
| 348 |
-
/* Span, style for the box shown on hovering the info icon */
|
| 349 |
-
.sk-estimator-doc-link span {
|
| 350 |
-
display: none;
|
| 351 |
-
z-index: 9999;
|
| 352 |
-
position: relative;
|
| 353 |
-
font-weight: normal;
|
| 354 |
-
right: .2ex;
|
| 355 |
-
padding: .5ex;
|
| 356 |
-
margin: .5ex;
|
| 357 |
-
width: min-content;
|
| 358 |
-
min-width: 20ex;
|
| 359 |
-
max-width: 50ex;
|
| 360 |
-
color: var(--sklearn-color-text);
|
| 361 |
-
box-shadow: 2pt 2pt 4pt #999;
|
| 362 |
-
/* unfitted */
|
| 363 |
-
background: var(--sklearn-color-unfitted-level-0);
|
| 364 |
-
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
.sk-estimator-doc-link.fitted span {
|
| 368 |
-
/* fitted */
|
| 369 |
-
background: var(--sklearn-color-fitted-level-0);
|
| 370 |
-
border: var(--sklearn-color-fitted-level-3);
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
.sk-estimator-doc-link:hover span {
|
| 374 |
-
display: block;
|
| 375 |
-
}
|
| 376 |
-
|
| 377 |
-
/* "?"-specific style due to the `<a>` HTML tag */
|
| 378 |
-
|
| 379 |
-
#sk-container-id-2 a.estimator_doc_link {
|
| 380 |
-
float: right;
|
| 381 |
-
font-size: 1rem;
|
| 382 |
-
line-height: 1em;
|
| 383 |
-
font-family: monospace;
|
| 384 |
-
background-color: var(--sklearn-color-background);
|
| 385 |
-
border-radius: 1rem;
|
| 386 |
-
height: 1rem;
|
| 387 |
-
width: 1rem;
|
| 388 |
-
text-decoration: none;
|
| 389 |
-
/* unfitted */
|
| 390 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 391 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
#sk-container-id-2 a.estimator_doc_link.fitted {
|
| 395 |
-
/* fitted */
|
| 396 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 397 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
/* On hover */
|
| 401 |
-
#sk-container-id-2 a.estimator_doc_link:hover {
|
| 402 |
-
/* unfitted */
|
| 403 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 404 |
-
color: var(--sklearn-color-background);
|
| 405 |
-
text-decoration: none;
|
| 406 |
-
}
|
| 407 |
-
|
| 408 |
-
#sk-container-id-2 a.estimator_doc_link.fitted:hover {
|
| 409 |
-
/* fitted */
|
| 410 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 411 |
-
}
|
| 412 |
-
</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
|
| 413 |
-
</body>
|
| 414 |
-
</html>
|
| 415 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34769333
|
| 18 |
-
model_uuid: 1223b8cf87ee4fae9c34ac54eeef9eb4
|
| 19 |
-
run_id: 39324cef8f2443b7b4b77eefe61ef6ca
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-30 16:26:16.224496'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:6b51c46f274e74ab79fd748cbe4fa01c5216d66f347bd96f71cfedf31015152f
|
| 3 |
-
size 34769333
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/39324cef8f2443b7b4b77eefe61ef6ca/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/estimator.html
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<!DOCTYPE html>
|
| 3 |
-
<html lang="en">
|
| 4 |
-
<head>
|
| 5 |
-
<meta charset="UTF-8"/>
|
| 6 |
-
</head>
|
| 7 |
-
<body>
|
| 8 |
-
<style>#sk-container-id-2 {
|
| 9 |
-
/* Definition of color scheme common for light and dark mode */
|
| 10 |
-
--sklearn-color-text: black;
|
| 11 |
-
--sklearn-color-line: gray;
|
| 12 |
-
/* Definition of color scheme for unfitted estimators */
|
| 13 |
-
--sklearn-color-unfitted-level-0: #fff5e6;
|
| 14 |
-
--sklearn-color-unfitted-level-1: #f6e4d2;
|
| 15 |
-
--sklearn-color-unfitted-level-2: #ffe0b3;
|
| 16 |
-
--sklearn-color-unfitted-level-3: chocolate;
|
| 17 |
-
/* Definition of color scheme for fitted estimators */
|
| 18 |
-
--sklearn-color-fitted-level-0: #f0f8ff;
|
| 19 |
-
--sklearn-color-fitted-level-1: #d4ebff;
|
| 20 |
-
--sklearn-color-fitted-level-2: #b3dbfd;
|
| 21 |
-
--sklearn-color-fitted-level-3: cornflowerblue;
|
| 22 |
-
|
| 23 |
-
/* Specific color for light theme */
|
| 24 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 25 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
|
| 26 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 27 |
-
--sklearn-color-icon: #696969;
|
| 28 |
-
|
| 29 |
-
@media (prefers-color-scheme: dark) {
|
| 30 |
-
/* Redefinition of color scheme for dark theme */
|
| 31 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 32 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
|
| 33 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 34 |
-
--sklearn-color-icon: #878787;
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
#sk-container-id-2 {
|
| 39 |
-
color: var(--sklearn-color-text);
|
| 40 |
-
}
|
| 41 |
-
|
| 42 |
-
#sk-container-id-2 pre {
|
| 43 |
-
padding: 0;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
#sk-container-id-2 input.sk-hidden--visually {
|
| 47 |
-
border: 0;
|
| 48 |
-
clip: rect(1px 1px 1px 1px);
|
| 49 |
-
clip: rect(1px, 1px, 1px, 1px);
|
| 50 |
-
height: 1px;
|
| 51 |
-
margin: -1px;
|
| 52 |
-
overflow: hidden;
|
| 53 |
-
padding: 0;
|
| 54 |
-
position: absolute;
|
| 55 |
-
width: 1px;
|
| 56 |
-
}
|
| 57 |
-
|
| 58 |
-
#sk-container-id-2 div.sk-dashed-wrapped {
|
| 59 |
-
border: 1px dashed var(--sklearn-color-line);
|
| 60 |
-
margin: 0 0.4em 0.5em 0.4em;
|
| 61 |
-
box-sizing: border-box;
|
| 62 |
-
padding-bottom: 0.4em;
|
| 63 |
-
background-color: var(--sklearn-color-background);
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
#sk-container-id-2 div.sk-container {
|
| 67 |
-
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
| 68 |
-
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
| 69 |
-
so we also need the `!important` here to be able to override the
|
| 70 |
-
default hidden behavior on the sphinx rendered scikit-learn.org.
|
| 71 |
-
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
| 72 |
-
display: inline-block !important;
|
| 73 |
-
position: relative;
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
#sk-container-id-2 div.sk-text-repr-fallback {
|
| 77 |
-
display: none;
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
div.sk-parallel-item,
|
| 81 |
-
div.sk-serial,
|
| 82 |
-
div.sk-item {
|
| 83 |
-
/* draw centered vertical line to link estimators */
|
| 84 |
-
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
| 85 |
-
background-size: 2px 100%;
|
| 86 |
-
background-repeat: no-repeat;
|
| 87 |
-
background-position: center center;
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
/* Parallel-specific style estimator block */
|
| 91 |
-
|
| 92 |
-
#sk-container-id-2 div.sk-parallel-item::after {
|
| 93 |
-
content: "";
|
| 94 |
-
width: 100%;
|
| 95 |
-
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
| 96 |
-
flex-grow: 1;
|
| 97 |
-
}
|
| 98 |
-
|
| 99 |
-
#sk-container-id-2 div.sk-parallel {
|
| 100 |
-
display: flex;
|
| 101 |
-
align-items: stretch;
|
| 102 |
-
justify-content: center;
|
| 103 |
-
background-color: var(--sklearn-color-background);
|
| 104 |
-
position: relative;
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
#sk-container-id-2 div.sk-parallel-item {
|
| 108 |
-
display: flex;
|
| 109 |
-
flex-direction: column;
|
| 110 |
-
}
|
| 111 |
-
|
| 112 |
-
#sk-container-id-2 div.sk-parallel-item:first-child::after {
|
| 113 |
-
align-self: flex-end;
|
| 114 |
-
width: 50%;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
#sk-container-id-2 div.sk-parallel-item:last-child::after {
|
| 118 |
-
align-self: flex-start;
|
| 119 |
-
width: 50%;
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
#sk-container-id-2 div.sk-parallel-item:only-child::after {
|
| 123 |
-
width: 0;
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
/* Serial-specific style estimator block */
|
| 127 |
-
|
| 128 |
-
#sk-container-id-2 div.sk-serial {
|
| 129 |
-
display: flex;
|
| 130 |
-
flex-direction: column;
|
| 131 |
-
align-items: center;
|
| 132 |
-
background-color: var(--sklearn-color-background);
|
| 133 |
-
padding-right: 1em;
|
| 134 |
-
padding-left: 1em;
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 139 |
-
clickable and can be expanded/collapsed.
|
| 140 |
-
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 141 |
-
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 142 |
-
*/
|
| 143 |
-
|
| 144 |
-
/* Pipeline and ColumnTransformer style (default) */
|
| 145 |
-
|
| 146 |
-
#sk-container-id-2 div.sk-toggleable {
|
| 147 |
-
/* Default theme specific background. It is overwritten whether we have a
|
| 148 |
-
specific estimator or a Pipeline/ColumnTransformer */
|
| 149 |
-
background-color: var(--sklearn-color-background);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
/* Toggleable label */
|
| 153 |
-
#sk-container-id-2 label.sk-toggleable__label {
|
| 154 |
-
cursor: pointer;
|
| 155 |
-
display: block;
|
| 156 |
-
width: 100%;
|
| 157 |
-
margin-bottom: 0;
|
| 158 |
-
padding: 0.5em;
|
| 159 |
-
box-sizing: border-box;
|
| 160 |
-
text-align: center;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:before {
|
| 164 |
-
/* Arrow on the left of the label */
|
| 165 |
-
content: "▸";
|
| 166 |
-
float: left;
|
| 167 |
-
margin-right: 0.25em;
|
| 168 |
-
color: var(--sklearn-color-icon);
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
|
| 172 |
-
color: var(--sklearn-color-text);
|
| 173 |
-
}
|
| 174 |
-
|
| 175 |
-
/* Toggleable content - dropdown */
|
| 176 |
-
|
| 177 |
-
#sk-container-id-2 div.sk-toggleable__content {
|
| 178 |
-
max-height: 0;
|
| 179 |
-
max-width: 0;
|
| 180 |
-
overflow: hidden;
|
| 181 |
-
text-align: left;
|
| 182 |
-
/* unfitted */
|
| 183 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 184 |
-
}
|
| 185 |
-
|
| 186 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted {
|
| 187 |
-
/* fitted */
|
| 188 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 189 |
-
}
|
| 190 |
-
|
| 191 |
-
#sk-container-id-2 div.sk-toggleable__content pre {
|
| 192 |
-
margin: 0.2em;
|
| 193 |
-
border-radius: 0.25em;
|
| 194 |
-
color: var(--sklearn-color-text);
|
| 195 |
-
/* unfitted */
|
| 196 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted pre {
|
| 200 |
-
/* unfitted */
|
| 201 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 202 |
-
}
|
| 203 |
-
|
| 204 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
| 205 |
-
/* Expand drop-down */
|
| 206 |
-
max-height: 200px;
|
| 207 |
-
max-width: 100%;
|
| 208 |
-
overflow: auto;
|
| 209 |
-
}
|
| 210 |
-
|
| 211 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
| 212 |
-
content: "▾";
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
/* Pipeline/ColumnTransformer-specific style */
|
| 216 |
-
|
| 217 |
-
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 218 |
-
color: var(--sklearn-color-text);
|
| 219 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 223 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
/* Estimator-specific style */
|
| 227 |
-
|
| 228 |
-
/* Colorize estimator box */
|
| 229 |
-
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 230 |
-
/* unfitted */
|
| 231 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 232 |
-
}
|
| 233 |
-
|
| 234 |
-
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 235 |
-
/* fitted */
|
| 236 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
|
| 240 |
-
#sk-container-id-2 div.sk-label label {
|
| 241 |
-
/* The background is the default theme color */
|
| 242 |
-
color: var(--sklearn-color-text-on-default-background);
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
/* On hover, darken the color of the background */
|
| 246 |
-
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
|
| 247 |
-
color: var(--sklearn-color-text);
|
| 248 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
/* Label box, darken color on hover, fitted */
|
| 252 |
-
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
| 253 |
-
color: var(--sklearn-color-text);
|
| 254 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 255 |
-
}
|
| 256 |
-
|
| 257 |
-
/* Estimator label */
|
| 258 |
-
|
| 259 |
-
#sk-container-id-2 div.sk-label label {
|
| 260 |
-
font-family: monospace;
|
| 261 |
-
font-weight: bold;
|
| 262 |
-
display: inline-block;
|
| 263 |
-
line-height: 1.2em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
#sk-container-id-2 div.sk-label-container {
|
| 267 |
-
text-align: center;
|
| 268 |
-
}
|
| 269 |
-
|
| 270 |
-
/* Estimator-specific */
|
| 271 |
-
#sk-container-id-2 div.sk-estimator {
|
| 272 |
-
font-family: monospace;
|
| 273 |
-
border: 1px dotted var(--sklearn-color-border-box);
|
| 274 |
-
border-radius: 0.25em;
|
| 275 |
-
box-sizing: border-box;
|
| 276 |
-
margin-bottom: 0.5em;
|
| 277 |
-
/* unfitted */
|
| 278 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
#sk-container-id-2 div.sk-estimator.fitted {
|
| 282 |
-
/* fitted */
|
| 283 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 284 |
-
}
|
| 285 |
-
|
| 286 |
-
/* on hover */
|
| 287 |
-
#sk-container-id-2 div.sk-estimator:hover {
|
| 288 |
-
/* unfitted */
|
| 289 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 290 |
-
}
|
| 291 |
-
|
| 292 |
-
#sk-container-id-2 div.sk-estimator.fitted:hover {
|
| 293 |
-
/* fitted */
|
| 294 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 295 |
-
}
|
| 296 |
-
|
| 297 |
-
/* Specification for estimator info (e.g. "i" and "?") */
|
| 298 |
-
|
| 299 |
-
/* Common style for "i" and "?" */
|
| 300 |
-
|
| 301 |
-
.sk-estimator-doc-link,
|
| 302 |
-
a:link.sk-estimator-doc-link,
|
| 303 |
-
a:visited.sk-estimator-doc-link {
|
| 304 |
-
float: right;
|
| 305 |
-
font-size: smaller;
|
| 306 |
-
line-height: 1em;
|
| 307 |
-
font-family: monospace;
|
| 308 |
-
background-color: var(--sklearn-color-background);
|
| 309 |
-
border-radius: 1em;
|
| 310 |
-
height: 1em;
|
| 311 |
-
width: 1em;
|
| 312 |
-
text-decoration: none !important;
|
| 313 |
-
margin-left: 1ex;
|
| 314 |
-
/* unfitted */
|
| 315 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 316 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
.sk-estimator-doc-link.fitted,
|
| 320 |
-
a:link.sk-estimator-doc-link.fitted,
|
| 321 |
-
a:visited.sk-estimator-doc-link.fitted {
|
| 322 |
-
/* fitted */
|
| 323 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 324 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
/* On hover */
|
| 328 |
-
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 329 |
-
.sk-estimator-doc-link:hover,
|
| 330 |
-
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 331 |
-
.sk-estimator-doc-link:hover {
|
| 332 |
-
/* unfitted */
|
| 333 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 334 |
-
color: var(--sklearn-color-background);
|
| 335 |
-
text-decoration: none;
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 339 |
-
.sk-estimator-doc-link.fitted:hover,
|
| 340 |
-
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 341 |
-
.sk-estimator-doc-link.fitted:hover {
|
| 342 |
-
/* fitted */
|
| 343 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 344 |
-
color: var(--sklearn-color-background);
|
| 345 |
-
text-decoration: none;
|
| 346 |
-
}
|
| 347 |
-
|
| 348 |
-
/* Span, style for the box shown on hovering the info icon */
|
| 349 |
-
.sk-estimator-doc-link span {
|
| 350 |
-
display: none;
|
| 351 |
-
z-index: 9999;
|
| 352 |
-
position: relative;
|
| 353 |
-
font-weight: normal;
|
| 354 |
-
right: .2ex;
|
| 355 |
-
padding: .5ex;
|
| 356 |
-
margin: .5ex;
|
| 357 |
-
width: min-content;
|
| 358 |
-
min-width: 20ex;
|
| 359 |
-
max-width: 50ex;
|
| 360 |
-
color: var(--sklearn-color-text);
|
| 361 |
-
box-shadow: 2pt 2pt 4pt #999;
|
| 362 |
-
/* unfitted */
|
| 363 |
-
background: var(--sklearn-color-unfitted-level-0);
|
| 364 |
-
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
.sk-estimator-doc-link.fitted span {
|
| 368 |
-
/* fitted */
|
| 369 |
-
background: var(--sklearn-color-fitted-level-0);
|
| 370 |
-
border: var(--sklearn-color-fitted-level-3);
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
.sk-estimator-doc-link:hover span {
|
| 374 |
-
display: block;
|
| 375 |
-
}
|
| 376 |
-
|
| 377 |
-
/* "?"-specific style due to the `<a>` HTML tag */
|
| 378 |
-
|
| 379 |
-
#sk-container-id-2 a.estimator_doc_link {
|
| 380 |
-
float: right;
|
| 381 |
-
font-size: 1rem;
|
| 382 |
-
line-height: 1em;
|
| 383 |
-
font-family: monospace;
|
| 384 |
-
background-color: var(--sklearn-color-background);
|
| 385 |
-
border-radius: 1rem;
|
| 386 |
-
height: 1rem;
|
| 387 |
-
width: 1rem;
|
| 388 |
-
text-decoration: none;
|
| 389 |
-
/* unfitted */
|
| 390 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 391 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
#sk-container-id-2 a.estimator_doc_link.fitted {
|
| 395 |
-
/* fitted */
|
| 396 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 397 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
/* On hover */
|
| 401 |
-
#sk-container-id-2 a.estimator_doc_link:hover {
|
| 402 |
-
/* unfitted */
|
| 403 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 404 |
-
color: var(--sklearn-color-background);
|
| 405 |
-
text-decoration: none;
|
| 406 |
-
}
|
| 407 |
-
|
| 408 |
-
#sk-container-id-2 a.estimator_doc_link.fitted:hover {
|
| 409 |
-
/* fitted */
|
| 410 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 411 |
-
}
|
| 412 |
-
</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
|
| 413 |
-
</body>
|
| 414 |
-
</html>
|
| 415 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34769333
|
| 18 |
-
model_uuid: 8332f56e26444f36b4c0ca9a376792c0
|
| 19 |
-
run_id: 482f080397f8479f94024fbed2a3937a
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-29 21:39:32.284449'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:6b51c46f274e74ab79fd748cbe4fa01c5216d66f347bd96f71cfedf31015152f
|
| 3 |
-
size 34769333
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/482f080397f8479f94024fbed2a3937a/artifacts/model/requirements.txt
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mlflow==2.16.2
|
| 2 |
-
cloudpickle==3.0.0
|
| 3 |
-
numpy==1.26.2
|
| 4 |
-
pandas==2.2.2
|
| 5 |
-
psutil==5.9.4
|
| 6 |
-
scikit-learn==1.5.2
|
| 7 |
-
scipy==1.11.4
|
| 8 |
-
typing==3.7.4.3
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/estimator.html
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<!DOCTYPE html>
|
| 3 |
-
<html lang="en">
|
| 4 |
-
<head>
|
| 5 |
-
<meta charset="UTF-8"/>
|
| 6 |
-
</head>
|
| 7 |
-
<body>
|
| 8 |
-
<style>#sk-container-id-2 {
|
| 9 |
-
/* Definition of color scheme common for light and dark mode */
|
| 10 |
-
--sklearn-color-text: black;
|
| 11 |
-
--sklearn-color-line: gray;
|
| 12 |
-
/* Definition of color scheme for unfitted estimators */
|
| 13 |
-
--sklearn-color-unfitted-level-0: #fff5e6;
|
| 14 |
-
--sklearn-color-unfitted-level-1: #f6e4d2;
|
| 15 |
-
--sklearn-color-unfitted-level-2: #ffe0b3;
|
| 16 |
-
--sklearn-color-unfitted-level-3: chocolate;
|
| 17 |
-
/* Definition of color scheme for fitted estimators */
|
| 18 |
-
--sklearn-color-fitted-level-0: #f0f8ff;
|
| 19 |
-
--sklearn-color-fitted-level-1: #d4ebff;
|
| 20 |
-
--sklearn-color-fitted-level-2: #b3dbfd;
|
| 21 |
-
--sklearn-color-fitted-level-3: cornflowerblue;
|
| 22 |
-
|
| 23 |
-
/* Specific color for light theme */
|
| 24 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 25 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
|
| 26 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 27 |
-
--sklearn-color-icon: #696969;
|
| 28 |
-
|
| 29 |
-
@media (prefers-color-scheme: dark) {
|
| 30 |
-
/* Redefinition of color scheme for dark theme */
|
| 31 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 32 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
|
| 33 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 34 |
-
--sklearn-color-icon: #878787;
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
#sk-container-id-2 {
|
| 39 |
-
color: var(--sklearn-color-text);
|
| 40 |
-
}
|
| 41 |
-
|
| 42 |
-
#sk-container-id-2 pre {
|
| 43 |
-
padding: 0;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
#sk-container-id-2 input.sk-hidden--visually {
|
| 47 |
-
border: 0;
|
| 48 |
-
clip: rect(1px 1px 1px 1px);
|
| 49 |
-
clip: rect(1px, 1px, 1px, 1px);
|
| 50 |
-
height: 1px;
|
| 51 |
-
margin: -1px;
|
| 52 |
-
overflow: hidden;
|
| 53 |
-
padding: 0;
|
| 54 |
-
position: absolute;
|
| 55 |
-
width: 1px;
|
| 56 |
-
}
|
| 57 |
-
|
| 58 |
-
#sk-container-id-2 div.sk-dashed-wrapped {
|
| 59 |
-
border: 1px dashed var(--sklearn-color-line);
|
| 60 |
-
margin: 0 0.4em 0.5em 0.4em;
|
| 61 |
-
box-sizing: border-box;
|
| 62 |
-
padding-bottom: 0.4em;
|
| 63 |
-
background-color: var(--sklearn-color-background);
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
#sk-container-id-2 div.sk-container {
|
| 67 |
-
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
| 68 |
-
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
| 69 |
-
so we also need the `!important` here to be able to override the
|
| 70 |
-
default hidden behavior on the sphinx rendered scikit-learn.org.
|
| 71 |
-
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
| 72 |
-
display: inline-block !important;
|
| 73 |
-
position: relative;
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
#sk-container-id-2 div.sk-text-repr-fallback {
|
| 77 |
-
display: none;
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
div.sk-parallel-item,
|
| 81 |
-
div.sk-serial,
|
| 82 |
-
div.sk-item {
|
| 83 |
-
/* draw centered vertical line to link estimators */
|
| 84 |
-
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
| 85 |
-
background-size: 2px 100%;
|
| 86 |
-
background-repeat: no-repeat;
|
| 87 |
-
background-position: center center;
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
/* Parallel-specific style estimator block */
|
| 91 |
-
|
| 92 |
-
#sk-container-id-2 div.sk-parallel-item::after {
|
| 93 |
-
content: "";
|
| 94 |
-
width: 100%;
|
| 95 |
-
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
| 96 |
-
flex-grow: 1;
|
| 97 |
-
}
|
| 98 |
-
|
| 99 |
-
#sk-container-id-2 div.sk-parallel {
|
| 100 |
-
display: flex;
|
| 101 |
-
align-items: stretch;
|
| 102 |
-
justify-content: center;
|
| 103 |
-
background-color: var(--sklearn-color-background);
|
| 104 |
-
position: relative;
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
#sk-container-id-2 div.sk-parallel-item {
|
| 108 |
-
display: flex;
|
| 109 |
-
flex-direction: column;
|
| 110 |
-
}
|
| 111 |
-
|
| 112 |
-
#sk-container-id-2 div.sk-parallel-item:first-child::after {
|
| 113 |
-
align-self: flex-end;
|
| 114 |
-
width: 50%;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
#sk-container-id-2 div.sk-parallel-item:last-child::after {
|
| 118 |
-
align-self: flex-start;
|
| 119 |
-
width: 50%;
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
#sk-container-id-2 div.sk-parallel-item:only-child::after {
|
| 123 |
-
width: 0;
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
/* Serial-specific style estimator block */
|
| 127 |
-
|
| 128 |
-
#sk-container-id-2 div.sk-serial {
|
| 129 |
-
display: flex;
|
| 130 |
-
flex-direction: column;
|
| 131 |
-
align-items: center;
|
| 132 |
-
background-color: var(--sklearn-color-background);
|
| 133 |
-
padding-right: 1em;
|
| 134 |
-
padding-left: 1em;
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 139 |
-
clickable and can be expanded/collapsed.
|
| 140 |
-
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 141 |
-
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 142 |
-
*/
|
| 143 |
-
|
| 144 |
-
/* Pipeline and ColumnTransformer style (default) */
|
| 145 |
-
|
| 146 |
-
#sk-container-id-2 div.sk-toggleable {
|
| 147 |
-
/* Default theme specific background. It is overwritten whether we have a
|
| 148 |
-
specific estimator or a Pipeline/ColumnTransformer */
|
| 149 |
-
background-color: var(--sklearn-color-background);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
/* Toggleable label */
|
| 153 |
-
#sk-container-id-2 label.sk-toggleable__label {
|
| 154 |
-
cursor: pointer;
|
| 155 |
-
display: block;
|
| 156 |
-
width: 100%;
|
| 157 |
-
margin-bottom: 0;
|
| 158 |
-
padding: 0.5em;
|
| 159 |
-
box-sizing: border-box;
|
| 160 |
-
text-align: center;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:before {
|
| 164 |
-
/* Arrow on the left of the label */
|
| 165 |
-
content: "▸";
|
| 166 |
-
float: left;
|
| 167 |
-
margin-right: 0.25em;
|
| 168 |
-
color: var(--sklearn-color-icon);
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
|
| 172 |
-
color: var(--sklearn-color-text);
|
| 173 |
-
}
|
| 174 |
-
|
| 175 |
-
/* Toggleable content - dropdown */
|
| 176 |
-
|
| 177 |
-
#sk-container-id-2 div.sk-toggleable__content {
|
| 178 |
-
max-height: 0;
|
| 179 |
-
max-width: 0;
|
| 180 |
-
overflow: hidden;
|
| 181 |
-
text-align: left;
|
| 182 |
-
/* unfitted */
|
| 183 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 184 |
-
}
|
| 185 |
-
|
| 186 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted {
|
| 187 |
-
/* fitted */
|
| 188 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 189 |
-
}
|
| 190 |
-
|
| 191 |
-
#sk-container-id-2 div.sk-toggleable__content pre {
|
| 192 |
-
margin: 0.2em;
|
| 193 |
-
border-radius: 0.25em;
|
| 194 |
-
color: var(--sklearn-color-text);
|
| 195 |
-
/* unfitted */
|
| 196 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted pre {
|
| 200 |
-
/* unfitted */
|
| 201 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 202 |
-
}
|
| 203 |
-
|
| 204 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
| 205 |
-
/* Expand drop-down */
|
| 206 |
-
max-height: 200px;
|
| 207 |
-
max-width: 100%;
|
| 208 |
-
overflow: auto;
|
| 209 |
-
}
|
| 210 |
-
|
| 211 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
| 212 |
-
content: "▾";
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
/* Pipeline/ColumnTransformer-specific style */
|
| 216 |
-
|
| 217 |
-
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 218 |
-
color: var(--sklearn-color-text);
|
| 219 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 223 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
/* Estimator-specific style */
|
| 227 |
-
|
| 228 |
-
/* Colorize estimator box */
|
| 229 |
-
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 230 |
-
/* unfitted */
|
| 231 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 232 |
-
}
|
| 233 |
-
|
| 234 |
-
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 235 |
-
/* fitted */
|
| 236 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
|
| 240 |
-
#sk-container-id-2 div.sk-label label {
|
| 241 |
-
/* The background is the default theme color */
|
| 242 |
-
color: var(--sklearn-color-text-on-default-background);
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
/* On hover, darken the color of the background */
|
| 246 |
-
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
|
| 247 |
-
color: var(--sklearn-color-text);
|
| 248 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
/* Label box, darken color on hover, fitted */
|
| 252 |
-
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
| 253 |
-
color: var(--sklearn-color-text);
|
| 254 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 255 |
-
}
|
| 256 |
-
|
| 257 |
-
/* Estimator label */
|
| 258 |
-
|
| 259 |
-
#sk-container-id-2 div.sk-label label {
|
| 260 |
-
font-family: monospace;
|
| 261 |
-
font-weight: bold;
|
| 262 |
-
display: inline-block;
|
| 263 |
-
line-height: 1.2em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
#sk-container-id-2 div.sk-label-container {
|
| 267 |
-
text-align: center;
|
| 268 |
-
}
|
| 269 |
-
|
| 270 |
-
/* Estimator-specific */
|
| 271 |
-
#sk-container-id-2 div.sk-estimator {
|
| 272 |
-
font-family: monospace;
|
| 273 |
-
border: 1px dotted var(--sklearn-color-border-box);
|
| 274 |
-
border-radius: 0.25em;
|
| 275 |
-
box-sizing: border-box;
|
| 276 |
-
margin-bottom: 0.5em;
|
| 277 |
-
/* unfitted */
|
| 278 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
#sk-container-id-2 div.sk-estimator.fitted {
|
| 282 |
-
/* fitted */
|
| 283 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 284 |
-
}
|
| 285 |
-
|
| 286 |
-
/* on hover */
|
| 287 |
-
#sk-container-id-2 div.sk-estimator:hover {
|
| 288 |
-
/* unfitted */
|
| 289 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 290 |
-
}
|
| 291 |
-
|
| 292 |
-
#sk-container-id-2 div.sk-estimator.fitted:hover {
|
| 293 |
-
/* fitted */
|
| 294 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 295 |
-
}
|
| 296 |
-
|
| 297 |
-
/* Specification for estimator info (e.g. "i" and "?") */
|
| 298 |
-
|
| 299 |
-
/* Common style for "i" and "?" */
|
| 300 |
-
|
| 301 |
-
.sk-estimator-doc-link,
|
| 302 |
-
a:link.sk-estimator-doc-link,
|
| 303 |
-
a:visited.sk-estimator-doc-link {
|
| 304 |
-
float: right;
|
| 305 |
-
font-size: smaller;
|
| 306 |
-
line-height: 1em;
|
| 307 |
-
font-family: monospace;
|
| 308 |
-
background-color: var(--sklearn-color-background);
|
| 309 |
-
border-radius: 1em;
|
| 310 |
-
height: 1em;
|
| 311 |
-
width: 1em;
|
| 312 |
-
text-decoration: none !important;
|
| 313 |
-
margin-left: 1ex;
|
| 314 |
-
/* unfitted */
|
| 315 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 316 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
.sk-estimator-doc-link.fitted,
|
| 320 |
-
a:link.sk-estimator-doc-link.fitted,
|
| 321 |
-
a:visited.sk-estimator-doc-link.fitted {
|
| 322 |
-
/* fitted */
|
| 323 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 324 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
/* On hover */
|
| 328 |
-
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 329 |
-
.sk-estimator-doc-link:hover,
|
| 330 |
-
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 331 |
-
.sk-estimator-doc-link:hover {
|
| 332 |
-
/* unfitted */
|
| 333 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 334 |
-
color: var(--sklearn-color-background);
|
| 335 |
-
text-decoration: none;
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 339 |
-
.sk-estimator-doc-link.fitted:hover,
|
| 340 |
-
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 341 |
-
.sk-estimator-doc-link.fitted:hover {
|
| 342 |
-
/* fitted */
|
| 343 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 344 |
-
color: var(--sklearn-color-background);
|
| 345 |
-
text-decoration: none;
|
| 346 |
-
}
|
| 347 |
-
|
| 348 |
-
/* Span, style for the box shown on hovering the info icon */
|
| 349 |
-
.sk-estimator-doc-link span {
|
| 350 |
-
display: none;
|
| 351 |
-
z-index: 9999;
|
| 352 |
-
position: relative;
|
| 353 |
-
font-weight: normal;
|
| 354 |
-
right: .2ex;
|
| 355 |
-
padding: .5ex;
|
| 356 |
-
margin: .5ex;
|
| 357 |
-
width: min-content;
|
| 358 |
-
min-width: 20ex;
|
| 359 |
-
max-width: 50ex;
|
| 360 |
-
color: var(--sklearn-color-text);
|
| 361 |
-
box-shadow: 2pt 2pt 4pt #999;
|
| 362 |
-
/* unfitted */
|
| 363 |
-
background: var(--sklearn-color-unfitted-level-0);
|
| 364 |
-
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
.sk-estimator-doc-link.fitted span {
|
| 368 |
-
/* fitted */
|
| 369 |
-
background: var(--sklearn-color-fitted-level-0);
|
| 370 |
-
border: var(--sklearn-color-fitted-level-3);
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
.sk-estimator-doc-link:hover span {
|
| 374 |
-
display: block;
|
| 375 |
-
}
|
| 376 |
-
|
| 377 |
-
/* "?"-specific style due to the `<a>` HTML tag */
|
| 378 |
-
|
| 379 |
-
#sk-container-id-2 a.estimator_doc_link {
|
| 380 |
-
float: right;
|
| 381 |
-
font-size: 1rem;
|
| 382 |
-
line-height: 1em;
|
| 383 |
-
font-family: monospace;
|
| 384 |
-
background-color: var(--sklearn-color-background);
|
| 385 |
-
border-radius: 1rem;
|
| 386 |
-
height: 1rem;
|
| 387 |
-
width: 1rem;
|
| 388 |
-
text-decoration: none;
|
| 389 |
-
/* unfitted */
|
| 390 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 391 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
#sk-container-id-2 a.estimator_doc_link.fitted {
|
| 395 |
-
/* fitted */
|
| 396 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 397 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
/* On hover */
|
| 401 |
-
#sk-container-id-2 a.estimator_doc_link:hover {
|
| 402 |
-
/* unfitted */
|
| 403 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 404 |
-
color: var(--sklearn-color-background);
|
| 405 |
-
text-decoration: none;
|
| 406 |
-
}
|
| 407 |
-
|
| 408 |
-
#sk-container-id-2 a.estimator_doc_link.fitted:hover {
|
| 409 |
-
/* fitted */
|
| 410 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 411 |
-
}
|
| 412 |
-
</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
|
| 413 |
-
</body>
|
| 414 |
-
</html>
|
| 415 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34743349
|
| 18 |
-
model_uuid: 2ece0aa5720e4d60a908faa9d0e3d000
|
| 19 |
-
run_id: 5479a322736e4663944d983720a6648e
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-29 20:35:11.167642'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- cloudpickle==3.0.0
|
| 9 |
-
- numpy==1.26.2
|
| 10 |
-
- pandas==2.2.2
|
| 11 |
-
- psutil==5.9.4
|
| 12 |
-
- scikit-learn==1.5.2
|
| 13 |
-
- scipy==1.11.4
|
| 14 |
-
- typing==3.7.4.3
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:0bf0697af096e259e722ff4149548875d48514deded04ec7fac97f0eca1d5b20
|
| 3 |
-
size 34743349
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/5479a322736e4663944d983720a6648e/artifacts/model/requirements.txt
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mlflow==2.16.2
|
| 2 |
-
cloudpickle==3.0.0
|
| 3 |
-
numpy==1.26.2
|
| 4 |
-
pandas==2.2.2
|
| 5 |
-
psutil==5.9.4
|
| 6 |
-
scikit-learn==1.5.2
|
| 7 |
-
scipy==1.11.4
|
| 8 |
-
typing==3.7.4.3
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/estimator.html
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<!DOCTYPE html>
|
| 3 |
-
<html lang="en">
|
| 4 |
-
<head>
|
| 5 |
-
<meta charset="UTF-8"/>
|
| 6 |
-
</head>
|
| 7 |
-
<body>
|
| 8 |
-
<style>#sk-container-id-2 {
|
| 9 |
-
/* Definition of color scheme common for light and dark mode */
|
| 10 |
-
--sklearn-color-text: black;
|
| 11 |
-
--sklearn-color-line: gray;
|
| 12 |
-
/* Definition of color scheme for unfitted estimators */
|
| 13 |
-
--sklearn-color-unfitted-level-0: #fff5e6;
|
| 14 |
-
--sklearn-color-unfitted-level-1: #f6e4d2;
|
| 15 |
-
--sklearn-color-unfitted-level-2: #ffe0b3;
|
| 16 |
-
--sklearn-color-unfitted-level-3: chocolate;
|
| 17 |
-
/* Definition of color scheme for fitted estimators */
|
| 18 |
-
--sklearn-color-fitted-level-0: #f0f8ff;
|
| 19 |
-
--sklearn-color-fitted-level-1: #d4ebff;
|
| 20 |
-
--sklearn-color-fitted-level-2: #b3dbfd;
|
| 21 |
-
--sklearn-color-fitted-level-3: cornflowerblue;
|
| 22 |
-
|
| 23 |
-
/* Specific color for light theme */
|
| 24 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 25 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
|
| 26 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 27 |
-
--sklearn-color-icon: #696969;
|
| 28 |
-
|
| 29 |
-
@media (prefers-color-scheme: dark) {
|
| 30 |
-
/* Redefinition of color scheme for dark theme */
|
| 31 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 32 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
|
| 33 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 34 |
-
--sklearn-color-icon: #878787;
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
#sk-container-id-2 {
|
| 39 |
-
color: var(--sklearn-color-text);
|
| 40 |
-
}
|
| 41 |
-
|
| 42 |
-
#sk-container-id-2 pre {
|
| 43 |
-
padding: 0;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
#sk-container-id-2 input.sk-hidden--visually {
|
| 47 |
-
border: 0;
|
| 48 |
-
clip: rect(1px 1px 1px 1px);
|
| 49 |
-
clip: rect(1px, 1px, 1px, 1px);
|
| 50 |
-
height: 1px;
|
| 51 |
-
margin: -1px;
|
| 52 |
-
overflow: hidden;
|
| 53 |
-
padding: 0;
|
| 54 |
-
position: absolute;
|
| 55 |
-
width: 1px;
|
| 56 |
-
}
|
| 57 |
-
|
| 58 |
-
#sk-container-id-2 div.sk-dashed-wrapped {
|
| 59 |
-
border: 1px dashed var(--sklearn-color-line);
|
| 60 |
-
margin: 0 0.4em 0.5em 0.4em;
|
| 61 |
-
box-sizing: border-box;
|
| 62 |
-
padding-bottom: 0.4em;
|
| 63 |
-
background-color: var(--sklearn-color-background);
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
#sk-container-id-2 div.sk-container {
|
| 67 |
-
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
| 68 |
-
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
| 69 |
-
so we also need the `!important` here to be able to override the
|
| 70 |
-
default hidden behavior on the sphinx rendered scikit-learn.org.
|
| 71 |
-
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
| 72 |
-
display: inline-block !important;
|
| 73 |
-
position: relative;
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
#sk-container-id-2 div.sk-text-repr-fallback {
|
| 77 |
-
display: none;
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
div.sk-parallel-item,
|
| 81 |
-
div.sk-serial,
|
| 82 |
-
div.sk-item {
|
| 83 |
-
/* draw centered vertical line to link estimators */
|
| 84 |
-
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
| 85 |
-
background-size: 2px 100%;
|
| 86 |
-
background-repeat: no-repeat;
|
| 87 |
-
background-position: center center;
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
/* Parallel-specific style estimator block */
|
| 91 |
-
|
| 92 |
-
#sk-container-id-2 div.sk-parallel-item::after {
|
| 93 |
-
content: "";
|
| 94 |
-
width: 100%;
|
| 95 |
-
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
| 96 |
-
flex-grow: 1;
|
| 97 |
-
}
|
| 98 |
-
|
| 99 |
-
#sk-container-id-2 div.sk-parallel {
|
| 100 |
-
display: flex;
|
| 101 |
-
align-items: stretch;
|
| 102 |
-
justify-content: center;
|
| 103 |
-
background-color: var(--sklearn-color-background);
|
| 104 |
-
position: relative;
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
#sk-container-id-2 div.sk-parallel-item {
|
| 108 |
-
display: flex;
|
| 109 |
-
flex-direction: column;
|
| 110 |
-
}
|
| 111 |
-
|
| 112 |
-
#sk-container-id-2 div.sk-parallel-item:first-child::after {
|
| 113 |
-
align-self: flex-end;
|
| 114 |
-
width: 50%;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
#sk-container-id-2 div.sk-parallel-item:last-child::after {
|
| 118 |
-
align-self: flex-start;
|
| 119 |
-
width: 50%;
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
#sk-container-id-2 div.sk-parallel-item:only-child::after {
|
| 123 |
-
width: 0;
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
/* Serial-specific style estimator block */
|
| 127 |
-
|
| 128 |
-
#sk-container-id-2 div.sk-serial {
|
| 129 |
-
display: flex;
|
| 130 |
-
flex-direction: column;
|
| 131 |
-
align-items: center;
|
| 132 |
-
background-color: var(--sklearn-color-background);
|
| 133 |
-
padding-right: 1em;
|
| 134 |
-
padding-left: 1em;
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 139 |
-
clickable and can be expanded/collapsed.
|
| 140 |
-
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 141 |
-
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 142 |
-
*/
|
| 143 |
-
|
| 144 |
-
/* Pipeline and ColumnTransformer style (default) */
|
| 145 |
-
|
| 146 |
-
#sk-container-id-2 div.sk-toggleable {
|
| 147 |
-
/* Default theme specific background. It is overwritten whether we have a
|
| 148 |
-
specific estimator or a Pipeline/ColumnTransformer */
|
| 149 |
-
background-color: var(--sklearn-color-background);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
/* Toggleable label */
|
| 153 |
-
#sk-container-id-2 label.sk-toggleable__label {
|
| 154 |
-
cursor: pointer;
|
| 155 |
-
display: block;
|
| 156 |
-
width: 100%;
|
| 157 |
-
margin-bottom: 0;
|
| 158 |
-
padding: 0.5em;
|
| 159 |
-
box-sizing: border-box;
|
| 160 |
-
text-align: center;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:before {
|
| 164 |
-
/* Arrow on the left of the label */
|
| 165 |
-
content: "▸";
|
| 166 |
-
float: left;
|
| 167 |
-
margin-right: 0.25em;
|
| 168 |
-
color: var(--sklearn-color-icon);
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
|
| 172 |
-
color: var(--sklearn-color-text);
|
| 173 |
-
}
|
| 174 |
-
|
| 175 |
-
/* Toggleable content - dropdown */
|
| 176 |
-
|
| 177 |
-
#sk-container-id-2 div.sk-toggleable__content {
|
| 178 |
-
max-height: 0;
|
| 179 |
-
max-width: 0;
|
| 180 |
-
overflow: hidden;
|
| 181 |
-
text-align: left;
|
| 182 |
-
/* unfitted */
|
| 183 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 184 |
-
}
|
| 185 |
-
|
| 186 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted {
|
| 187 |
-
/* fitted */
|
| 188 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 189 |
-
}
|
| 190 |
-
|
| 191 |
-
#sk-container-id-2 div.sk-toggleable__content pre {
|
| 192 |
-
margin: 0.2em;
|
| 193 |
-
border-radius: 0.25em;
|
| 194 |
-
color: var(--sklearn-color-text);
|
| 195 |
-
/* unfitted */
|
| 196 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted pre {
|
| 200 |
-
/* unfitted */
|
| 201 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 202 |
-
}
|
| 203 |
-
|
| 204 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
| 205 |
-
/* Expand drop-down */
|
| 206 |
-
max-height: 200px;
|
| 207 |
-
max-width: 100%;
|
| 208 |
-
overflow: auto;
|
| 209 |
-
}
|
| 210 |
-
|
| 211 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
| 212 |
-
content: "▾";
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
/* Pipeline/ColumnTransformer-specific style */
|
| 216 |
-
|
| 217 |
-
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 218 |
-
color: var(--sklearn-color-text);
|
| 219 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 223 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
/* Estimator-specific style */
|
| 227 |
-
|
| 228 |
-
/* Colorize estimator box */
|
| 229 |
-
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 230 |
-
/* unfitted */
|
| 231 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 232 |
-
}
|
| 233 |
-
|
| 234 |
-
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 235 |
-
/* fitted */
|
| 236 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
|
| 240 |
-
#sk-container-id-2 div.sk-label label {
|
| 241 |
-
/* The background is the default theme color */
|
| 242 |
-
color: var(--sklearn-color-text-on-default-background);
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
/* On hover, darken the color of the background */
|
| 246 |
-
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
|
| 247 |
-
color: var(--sklearn-color-text);
|
| 248 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
/* Label box, darken color on hover, fitted */
|
| 252 |
-
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
| 253 |
-
color: var(--sklearn-color-text);
|
| 254 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 255 |
-
}
|
| 256 |
-
|
| 257 |
-
/* Estimator label */
|
| 258 |
-
|
| 259 |
-
#sk-container-id-2 div.sk-label label {
|
| 260 |
-
font-family: monospace;
|
| 261 |
-
font-weight: bold;
|
| 262 |
-
display: inline-block;
|
| 263 |
-
line-height: 1.2em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
#sk-container-id-2 div.sk-label-container {
|
| 267 |
-
text-align: center;
|
| 268 |
-
}
|
| 269 |
-
|
| 270 |
-
/* Estimator-specific */
|
| 271 |
-
#sk-container-id-2 div.sk-estimator {
|
| 272 |
-
font-family: monospace;
|
| 273 |
-
border: 1px dotted var(--sklearn-color-border-box);
|
| 274 |
-
border-radius: 0.25em;
|
| 275 |
-
box-sizing: border-box;
|
| 276 |
-
margin-bottom: 0.5em;
|
| 277 |
-
/* unfitted */
|
| 278 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
#sk-container-id-2 div.sk-estimator.fitted {
|
| 282 |
-
/* fitted */
|
| 283 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 284 |
-
}
|
| 285 |
-
|
| 286 |
-
/* on hover */
|
| 287 |
-
#sk-container-id-2 div.sk-estimator:hover {
|
| 288 |
-
/* unfitted */
|
| 289 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 290 |
-
}
|
| 291 |
-
|
| 292 |
-
#sk-container-id-2 div.sk-estimator.fitted:hover {
|
| 293 |
-
/* fitted */
|
| 294 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 295 |
-
}
|
| 296 |
-
|
| 297 |
-
/* Specification for estimator info (e.g. "i" and "?") */
|
| 298 |
-
|
| 299 |
-
/* Common style for "i" and "?" */
|
| 300 |
-
|
| 301 |
-
.sk-estimator-doc-link,
|
| 302 |
-
a:link.sk-estimator-doc-link,
|
| 303 |
-
a:visited.sk-estimator-doc-link {
|
| 304 |
-
float: right;
|
| 305 |
-
font-size: smaller;
|
| 306 |
-
line-height: 1em;
|
| 307 |
-
font-family: monospace;
|
| 308 |
-
background-color: var(--sklearn-color-background);
|
| 309 |
-
border-radius: 1em;
|
| 310 |
-
height: 1em;
|
| 311 |
-
width: 1em;
|
| 312 |
-
text-decoration: none !important;
|
| 313 |
-
margin-left: 1ex;
|
| 314 |
-
/* unfitted */
|
| 315 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 316 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
.sk-estimator-doc-link.fitted,
|
| 320 |
-
a:link.sk-estimator-doc-link.fitted,
|
| 321 |
-
a:visited.sk-estimator-doc-link.fitted {
|
| 322 |
-
/* fitted */
|
| 323 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 324 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
/* On hover */
|
| 328 |
-
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 329 |
-
.sk-estimator-doc-link:hover,
|
| 330 |
-
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 331 |
-
.sk-estimator-doc-link:hover {
|
| 332 |
-
/* unfitted */
|
| 333 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 334 |
-
color: var(--sklearn-color-background);
|
| 335 |
-
text-decoration: none;
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 339 |
-
.sk-estimator-doc-link.fitted:hover,
|
| 340 |
-
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 341 |
-
.sk-estimator-doc-link.fitted:hover {
|
| 342 |
-
/* fitted */
|
| 343 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 344 |
-
color: var(--sklearn-color-background);
|
| 345 |
-
text-decoration: none;
|
| 346 |
-
}
|
| 347 |
-
|
| 348 |
-
/* Span, style for the box shown on hovering the info icon */
|
| 349 |
-
.sk-estimator-doc-link span {
|
| 350 |
-
display: none;
|
| 351 |
-
z-index: 9999;
|
| 352 |
-
position: relative;
|
| 353 |
-
font-weight: normal;
|
| 354 |
-
right: .2ex;
|
| 355 |
-
padding: .5ex;
|
| 356 |
-
margin: .5ex;
|
| 357 |
-
width: min-content;
|
| 358 |
-
min-width: 20ex;
|
| 359 |
-
max-width: 50ex;
|
| 360 |
-
color: var(--sklearn-color-text);
|
| 361 |
-
box-shadow: 2pt 2pt 4pt #999;
|
| 362 |
-
/* unfitted */
|
| 363 |
-
background: var(--sklearn-color-unfitted-level-0);
|
| 364 |
-
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
.sk-estimator-doc-link.fitted span {
|
| 368 |
-
/* fitted */
|
| 369 |
-
background: var(--sklearn-color-fitted-level-0);
|
| 370 |
-
border: var(--sklearn-color-fitted-level-3);
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
.sk-estimator-doc-link:hover span {
|
| 374 |
-
display: block;
|
| 375 |
-
}
|
| 376 |
-
|
| 377 |
-
/* "?"-specific style due to the `<a>` HTML tag */
|
| 378 |
-
|
| 379 |
-
#sk-container-id-2 a.estimator_doc_link {
|
| 380 |
-
float: right;
|
| 381 |
-
font-size: 1rem;
|
| 382 |
-
line-height: 1em;
|
| 383 |
-
font-family: monospace;
|
| 384 |
-
background-color: var(--sklearn-color-background);
|
| 385 |
-
border-radius: 1rem;
|
| 386 |
-
height: 1rem;
|
| 387 |
-
width: 1rem;
|
| 388 |
-
text-decoration: none;
|
| 389 |
-
/* unfitted */
|
| 390 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 391 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
#sk-container-id-2 a.estimator_doc_link.fitted {
|
| 395 |
-
/* fitted */
|
| 396 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 397 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
/* On hover */
|
| 401 |
-
#sk-container-id-2 a.estimator_doc_link:hover {
|
| 402 |
-
/* unfitted */
|
| 403 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 404 |
-
color: var(--sklearn-color-background);
|
| 405 |
-
text-decoration: none;
|
| 406 |
-
}
|
| 407 |
-
|
| 408 |
-
#sk-container-id-2 a.estimator_doc_link.fitted:hover {
|
| 409 |
-
/* fitted */
|
| 410 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 411 |
-
}
|
| 412 |
-
</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
|
| 413 |
-
</body>
|
| 414 |
-
</html>
|
| 415 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34762837
|
| 18 |
-
model_uuid: d6a4b42658cb4824b3125c7ec63d1ed4
|
| 19 |
-
run_id: d256fba7a7fe43c39749afef37154210
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-29 20:41:11.947759'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- cloudpickle==3.0.0
|
| 9 |
-
- numpy==1.26.2
|
| 10 |
-
- pandas==2.2.2
|
| 11 |
-
- psutil==5.9.4
|
| 12 |
-
- scikit-learn==1.5.2
|
| 13 |
-
- scipy==1.11.4
|
| 14 |
-
- typing==3.7.4.3
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:72ba8a18da708a814986b0edbe6b5a4a7e931fea667acabeb4040aee0c038e02
|
| 3 |
-
size 34762837
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/d256fba7a7fe43c39749afef37154210/artifacts/model/requirements.txt
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mlflow==2.16.2
|
| 2 |
-
cloudpickle==3.0.0
|
| 3 |
-
numpy==1.26.2
|
| 4 |
-
pandas==2.2.2
|
| 5 |
-
psutil==5.9.4
|
| 6 |
-
scikit-learn==1.5.2
|
| 7 |
-
scipy==1.11.4
|
| 8 |
-
typing==3.7.4.3
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/estimator.html
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<!DOCTYPE html>
|
| 3 |
-
<html lang="en">
|
| 4 |
-
<head>
|
| 5 |
-
<meta charset="UTF-8"/>
|
| 6 |
-
</head>
|
| 7 |
-
<body>
|
| 8 |
-
<style>#sk-container-id-2 {
|
| 9 |
-
/* Definition of color scheme common for light and dark mode */
|
| 10 |
-
--sklearn-color-text: black;
|
| 11 |
-
--sklearn-color-line: gray;
|
| 12 |
-
/* Definition of color scheme for unfitted estimators */
|
| 13 |
-
--sklearn-color-unfitted-level-0: #fff5e6;
|
| 14 |
-
--sklearn-color-unfitted-level-1: #f6e4d2;
|
| 15 |
-
--sklearn-color-unfitted-level-2: #ffe0b3;
|
| 16 |
-
--sklearn-color-unfitted-level-3: chocolate;
|
| 17 |
-
/* Definition of color scheme for fitted estimators */
|
| 18 |
-
--sklearn-color-fitted-level-0: #f0f8ff;
|
| 19 |
-
--sklearn-color-fitted-level-1: #d4ebff;
|
| 20 |
-
--sklearn-color-fitted-level-2: #b3dbfd;
|
| 21 |
-
--sklearn-color-fitted-level-3: cornflowerblue;
|
| 22 |
-
|
| 23 |
-
/* Specific color for light theme */
|
| 24 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 25 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
|
| 26 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 27 |
-
--sklearn-color-icon: #696969;
|
| 28 |
-
|
| 29 |
-
@media (prefers-color-scheme: dark) {
|
| 30 |
-
/* Redefinition of color scheme for dark theme */
|
| 31 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 32 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
|
| 33 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 34 |
-
--sklearn-color-icon: #878787;
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
#sk-container-id-2 {
|
| 39 |
-
color: var(--sklearn-color-text);
|
| 40 |
-
}
|
| 41 |
-
|
| 42 |
-
#sk-container-id-2 pre {
|
| 43 |
-
padding: 0;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
#sk-container-id-2 input.sk-hidden--visually {
|
| 47 |
-
border: 0;
|
| 48 |
-
clip: rect(1px 1px 1px 1px);
|
| 49 |
-
clip: rect(1px, 1px, 1px, 1px);
|
| 50 |
-
height: 1px;
|
| 51 |
-
margin: -1px;
|
| 52 |
-
overflow: hidden;
|
| 53 |
-
padding: 0;
|
| 54 |
-
position: absolute;
|
| 55 |
-
width: 1px;
|
| 56 |
-
}
|
| 57 |
-
|
| 58 |
-
#sk-container-id-2 div.sk-dashed-wrapped {
|
| 59 |
-
border: 1px dashed var(--sklearn-color-line);
|
| 60 |
-
margin: 0 0.4em 0.5em 0.4em;
|
| 61 |
-
box-sizing: border-box;
|
| 62 |
-
padding-bottom: 0.4em;
|
| 63 |
-
background-color: var(--sklearn-color-background);
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
#sk-container-id-2 div.sk-container {
|
| 67 |
-
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
| 68 |
-
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
| 69 |
-
so we also need the `!important` here to be able to override the
|
| 70 |
-
default hidden behavior on the sphinx rendered scikit-learn.org.
|
| 71 |
-
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
| 72 |
-
display: inline-block !important;
|
| 73 |
-
position: relative;
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
#sk-container-id-2 div.sk-text-repr-fallback {
|
| 77 |
-
display: none;
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
div.sk-parallel-item,
|
| 81 |
-
div.sk-serial,
|
| 82 |
-
div.sk-item {
|
| 83 |
-
/* draw centered vertical line to link estimators */
|
| 84 |
-
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
| 85 |
-
background-size: 2px 100%;
|
| 86 |
-
background-repeat: no-repeat;
|
| 87 |
-
background-position: center center;
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
/* Parallel-specific style estimator block */
|
| 91 |
-
|
| 92 |
-
#sk-container-id-2 div.sk-parallel-item::after {
|
| 93 |
-
content: "";
|
| 94 |
-
width: 100%;
|
| 95 |
-
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
| 96 |
-
flex-grow: 1;
|
| 97 |
-
}
|
| 98 |
-
|
| 99 |
-
#sk-container-id-2 div.sk-parallel {
|
| 100 |
-
display: flex;
|
| 101 |
-
align-items: stretch;
|
| 102 |
-
justify-content: center;
|
| 103 |
-
background-color: var(--sklearn-color-background);
|
| 104 |
-
position: relative;
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
#sk-container-id-2 div.sk-parallel-item {
|
| 108 |
-
display: flex;
|
| 109 |
-
flex-direction: column;
|
| 110 |
-
}
|
| 111 |
-
|
| 112 |
-
#sk-container-id-2 div.sk-parallel-item:first-child::after {
|
| 113 |
-
align-self: flex-end;
|
| 114 |
-
width: 50%;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
#sk-container-id-2 div.sk-parallel-item:last-child::after {
|
| 118 |
-
align-self: flex-start;
|
| 119 |
-
width: 50%;
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
#sk-container-id-2 div.sk-parallel-item:only-child::after {
|
| 123 |
-
width: 0;
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
/* Serial-specific style estimator block */
|
| 127 |
-
|
| 128 |
-
#sk-container-id-2 div.sk-serial {
|
| 129 |
-
display: flex;
|
| 130 |
-
flex-direction: column;
|
| 131 |
-
align-items: center;
|
| 132 |
-
background-color: var(--sklearn-color-background);
|
| 133 |
-
padding-right: 1em;
|
| 134 |
-
padding-left: 1em;
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 139 |
-
clickable and can be expanded/collapsed.
|
| 140 |
-
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 141 |
-
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 142 |
-
*/
|
| 143 |
-
|
| 144 |
-
/* Pipeline and ColumnTransformer style (default) */
|
| 145 |
-
|
| 146 |
-
#sk-container-id-2 div.sk-toggleable {
|
| 147 |
-
/* Default theme specific background. It is overwritten whether we have a
|
| 148 |
-
specific estimator or a Pipeline/ColumnTransformer */
|
| 149 |
-
background-color: var(--sklearn-color-background);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
/* Toggleable label */
|
| 153 |
-
#sk-container-id-2 label.sk-toggleable__label {
|
| 154 |
-
cursor: pointer;
|
| 155 |
-
display: block;
|
| 156 |
-
width: 100%;
|
| 157 |
-
margin-bottom: 0;
|
| 158 |
-
padding: 0.5em;
|
| 159 |
-
box-sizing: border-box;
|
| 160 |
-
text-align: center;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:before {
|
| 164 |
-
/* Arrow on the left of the label */
|
| 165 |
-
content: "▸";
|
| 166 |
-
float: left;
|
| 167 |
-
margin-right: 0.25em;
|
| 168 |
-
color: var(--sklearn-color-icon);
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
|
| 172 |
-
color: var(--sklearn-color-text);
|
| 173 |
-
}
|
| 174 |
-
|
| 175 |
-
/* Toggleable content - dropdown */
|
| 176 |
-
|
| 177 |
-
#sk-container-id-2 div.sk-toggleable__content {
|
| 178 |
-
max-height: 0;
|
| 179 |
-
max-width: 0;
|
| 180 |
-
overflow: hidden;
|
| 181 |
-
text-align: left;
|
| 182 |
-
/* unfitted */
|
| 183 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 184 |
-
}
|
| 185 |
-
|
| 186 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted {
|
| 187 |
-
/* fitted */
|
| 188 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 189 |
-
}
|
| 190 |
-
|
| 191 |
-
#sk-container-id-2 div.sk-toggleable__content pre {
|
| 192 |
-
margin: 0.2em;
|
| 193 |
-
border-radius: 0.25em;
|
| 194 |
-
color: var(--sklearn-color-text);
|
| 195 |
-
/* unfitted */
|
| 196 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted pre {
|
| 200 |
-
/* unfitted */
|
| 201 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 202 |
-
}
|
| 203 |
-
|
| 204 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
| 205 |
-
/* Expand drop-down */
|
| 206 |
-
max-height: 200px;
|
| 207 |
-
max-width: 100%;
|
| 208 |
-
overflow: auto;
|
| 209 |
-
}
|
| 210 |
-
|
| 211 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
| 212 |
-
content: "▾";
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
/* Pipeline/ColumnTransformer-specific style */
|
| 216 |
-
|
| 217 |
-
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 218 |
-
color: var(--sklearn-color-text);
|
| 219 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 223 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
/* Estimator-specific style */
|
| 227 |
-
|
| 228 |
-
/* Colorize estimator box */
|
| 229 |
-
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 230 |
-
/* unfitted */
|
| 231 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 232 |
-
}
|
| 233 |
-
|
| 234 |
-
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 235 |
-
/* fitted */
|
| 236 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
|
| 240 |
-
#sk-container-id-2 div.sk-label label {
|
| 241 |
-
/* The background is the default theme color */
|
| 242 |
-
color: var(--sklearn-color-text-on-default-background);
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
/* On hover, darken the color of the background */
|
| 246 |
-
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
|
| 247 |
-
color: var(--sklearn-color-text);
|
| 248 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
/* Label box, darken color on hover, fitted */
|
| 252 |
-
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
| 253 |
-
color: var(--sklearn-color-text);
|
| 254 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 255 |
-
}
|
| 256 |
-
|
| 257 |
-
/* Estimator label */
|
| 258 |
-
|
| 259 |
-
#sk-container-id-2 div.sk-label label {
|
| 260 |
-
font-family: monospace;
|
| 261 |
-
font-weight: bold;
|
| 262 |
-
display: inline-block;
|
| 263 |
-
line-height: 1.2em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
#sk-container-id-2 div.sk-label-container {
|
| 267 |
-
text-align: center;
|
| 268 |
-
}
|
| 269 |
-
|
| 270 |
-
/* Estimator-specific */
|
| 271 |
-
#sk-container-id-2 div.sk-estimator {
|
| 272 |
-
font-family: monospace;
|
| 273 |
-
border: 1px dotted var(--sklearn-color-border-box);
|
| 274 |
-
border-radius: 0.25em;
|
| 275 |
-
box-sizing: border-box;
|
| 276 |
-
margin-bottom: 0.5em;
|
| 277 |
-
/* unfitted */
|
| 278 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
#sk-container-id-2 div.sk-estimator.fitted {
|
| 282 |
-
/* fitted */
|
| 283 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 284 |
-
}
|
| 285 |
-
|
| 286 |
-
/* on hover */
|
| 287 |
-
#sk-container-id-2 div.sk-estimator:hover {
|
| 288 |
-
/* unfitted */
|
| 289 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 290 |
-
}
|
| 291 |
-
|
| 292 |
-
#sk-container-id-2 div.sk-estimator.fitted:hover {
|
| 293 |
-
/* fitted */
|
| 294 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 295 |
-
}
|
| 296 |
-
|
| 297 |
-
/* Specification for estimator info (e.g. "i" and "?") */
|
| 298 |
-
|
| 299 |
-
/* Common style for "i" and "?" */
|
| 300 |
-
|
| 301 |
-
.sk-estimator-doc-link,
|
| 302 |
-
a:link.sk-estimator-doc-link,
|
| 303 |
-
a:visited.sk-estimator-doc-link {
|
| 304 |
-
float: right;
|
| 305 |
-
font-size: smaller;
|
| 306 |
-
line-height: 1em;
|
| 307 |
-
font-family: monospace;
|
| 308 |
-
background-color: var(--sklearn-color-background);
|
| 309 |
-
border-radius: 1em;
|
| 310 |
-
height: 1em;
|
| 311 |
-
width: 1em;
|
| 312 |
-
text-decoration: none !important;
|
| 313 |
-
margin-left: 1ex;
|
| 314 |
-
/* unfitted */
|
| 315 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 316 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
.sk-estimator-doc-link.fitted,
|
| 320 |
-
a:link.sk-estimator-doc-link.fitted,
|
| 321 |
-
a:visited.sk-estimator-doc-link.fitted {
|
| 322 |
-
/* fitted */
|
| 323 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 324 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
/* On hover */
|
| 328 |
-
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 329 |
-
.sk-estimator-doc-link:hover,
|
| 330 |
-
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 331 |
-
.sk-estimator-doc-link:hover {
|
| 332 |
-
/* unfitted */
|
| 333 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 334 |
-
color: var(--sklearn-color-background);
|
| 335 |
-
text-decoration: none;
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 339 |
-
.sk-estimator-doc-link.fitted:hover,
|
| 340 |
-
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 341 |
-
.sk-estimator-doc-link.fitted:hover {
|
| 342 |
-
/* fitted */
|
| 343 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 344 |
-
color: var(--sklearn-color-background);
|
| 345 |
-
text-decoration: none;
|
| 346 |
-
}
|
| 347 |
-
|
| 348 |
-
/* Span, style for the box shown on hovering the info icon */
|
| 349 |
-
.sk-estimator-doc-link span {
|
| 350 |
-
display: none;
|
| 351 |
-
z-index: 9999;
|
| 352 |
-
position: relative;
|
| 353 |
-
font-weight: normal;
|
| 354 |
-
right: .2ex;
|
| 355 |
-
padding: .5ex;
|
| 356 |
-
margin: .5ex;
|
| 357 |
-
width: min-content;
|
| 358 |
-
min-width: 20ex;
|
| 359 |
-
max-width: 50ex;
|
| 360 |
-
color: var(--sklearn-color-text);
|
| 361 |
-
box-shadow: 2pt 2pt 4pt #999;
|
| 362 |
-
/* unfitted */
|
| 363 |
-
background: var(--sklearn-color-unfitted-level-0);
|
| 364 |
-
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
.sk-estimator-doc-link.fitted span {
|
| 368 |
-
/* fitted */
|
| 369 |
-
background: var(--sklearn-color-fitted-level-0);
|
| 370 |
-
border: var(--sklearn-color-fitted-level-3);
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
.sk-estimator-doc-link:hover span {
|
| 374 |
-
display: block;
|
| 375 |
-
}
|
| 376 |
-
|
| 377 |
-
/* "?"-specific style due to the `<a>` HTML tag */
|
| 378 |
-
|
| 379 |
-
#sk-container-id-2 a.estimator_doc_link {
|
| 380 |
-
float: right;
|
| 381 |
-
font-size: 1rem;
|
| 382 |
-
line-height: 1em;
|
| 383 |
-
font-family: monospace;
|
| 384 |
-
background-color: var(--sklearn-color-background);
|
| 385 |
-
border-radius: 1rem;
|
| 386 |
-
height: 1rem;
|
| 387 |
-
width: 1rem;
|
| 388 |
-
text-decoration: none;
|
| 389 |
-
/* unfitted */
|
| 390 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 391 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
#sk-container-id-2 a.estimator_doc_link.fitted {
|
| 395 |
-
/* fitted */
|
| 396 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 397 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
/* On hover */
|
| 401 |
-
#sk-container-id-2 a.estimator_doc_link:hover {
|
| 402 |
-
/* unfitted */
|
| 403 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 404 |
-
color: var(--sklearn-color-background);
|
| 405 |
-
text-decoration: none;
|
| 406 |
-
}
|
| 407 |
-
|
| 408 |
-
#sk-container-id-2 a.estimator_doc_link.fitted:hover {
|
| 409 |
-
/* fitted */
|
| 410 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 411 |
-
}
|
| 412 |
-
</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
|
| 413 |
-
</body>
|
| 414 |
-
</html>
|
| 415 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34769333
|
| 18 |
-
model_uuid: 120c266f00cf4e34aca4337b65a1e372
|
| 19 |
-
run_id: faa3ad4458c7419a8fc7c85d44b5c475
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-30 11:43:18.288700'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- cloudpickle==3.0.0
|
| 9 |
-
- numpy==1.26.2
|
| 10 |
-
- pandas==2.2.2
|
| 11 |
-
- psutil==5.9.4
|
| 12 |
-
- scikit-learn==1.5.2
|
| 13 |
-
- scipy==1.11.4
|
| 14 |
-
- typing==3.7.4.3
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:6b51c46f274e74ab79fd748cbe4fa01c5216d66f347bd96f71cfedf31015152f
|
| 3 |
-
size 34769333
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/faa3ad4458c7419a8fc7c85d44b5c475/artifacts/model/requirements.txt
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mlflow==2.16.2
|
| 2 |
-
cloudpickle==3.0.0
|
| 3 |
-
numpy==1.26.2
|
| 4 |
-
pandas==2.2.2
|
| 5 |
-
psutil==5.9.4
|
| 6 |
-
scikit-learn==1.5.2
|
| 7 |
-
scipy==1.11.4
|
| 8 |
-
typing==3.7.4.3
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/estimator.html
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<!DOCTYPE html>
|
| 3 |
-
<html lang="en">
|
| 4 |
-
<head>
|
| 5 |
-
<meta charset="UTF-8"/>
|
| 6 |
-
</head>
|
| 7 |
-
<body>
|
| 8 |
-
<style>#sk-container-id-2 {
|
| 9 |
-
/* Definition of color scheme common for light and dark mode */
|
| 10 |
-
--sklearn-color-text: black;
|
| 11 |
-
--sklearn-color-line: gray;
|
| 12 |
-
/* Definition of color scheme for unfitted estimators */
|
| 13 |
-
--sklearn-color-unfitted-level-0: #fff5e6;
|
| 14 |
-
--sklearn-color-unfitted-level-1: #f6e4d2;
|
| 15 |
-
--sklearn-color-unfitted-level-2: #ffe0b3;
|
| 16 |
-
--sklearn-color-unfitted-level-3: chocolate;
|
| 17 |
-
/* Definition of color scheme for fitted estimators */
|
| 18 |
-
--sklearn-color-fitted-level-0: #f0f8ff;
|
| 19 |
-
--sklearn-color-fitted-level-1: #d4ebff;
|
| 20 |
-
--sklearn-color-fitted-level-2: #b3dbfd;
|
| 21 |
-
--sklearn-color-fitted-level-3: cornflowerblue;
|
| 22 |
-
|
| 23 |
-
/* Specific color for light theme */
|
| 24 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 25 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
|
| 26 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
|
| 27 |
-
--sklearn-color-icon: #696969;
|
| 28 |
-
|
| 29 |
-
@media (prefers-color-scheme: dark) {
|
| 30 |
-
/* Redefinition of color scheme for dark theme */
|
| 31 |
-
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 32 |
-
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
|
| 33 |
-
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
|
| 34 |
-
--sklearn-color-icon: #878787;
|
| 35 |
-
}
|
| 36 |
-
}
|
| 37 |
-
|
| 38 |
-
#sk-container-id-2 {
|
| 39 |
-
color: var(--sklearn-color-text);
|
| 40 |
-
}
|
| 41 |
-
|
| 42 |
-
#sk-container-id-2 pre {
|
| 43 |
-
padding: 0;
|
| 44 |
-
}
|
| 45 |
-
|
| 46 |
-
#sk-container-id-2 input.sk-hidden--visually {
|
| 47 |
-
border: 0;
|
| 48 |
-
clip: rect(1px 1px 1px 1px);
|
| 49 |
-
clip: rect(1px, 1px, 1px, 1px);
|
| 50 |
-
height: 1px;
|
| 51 |
-
margin: -1px;
|
| 52 |
-
overflow: hidden;
|
| 53 |
-
padding: 0;
|
| 54 |
-
position: absolute;
|
| 55 |
-
width: 1px;
|
| 56 |
-
}
|
| 57 |
-
|
| 58 |
-
#sk-container-id-2 div.sk-dashed-wrapped {
|
| 59 |
-
border: 1px dashed var(--sklearn-color-line);
|
| 60 |
-
margin: 0 0.4em 0.5em 0.4em;
|
| 61 |
-
box-sizing: border-box;
|
| 62 |
-
padding-bottom: 0.4em;
|
| 63 |
-
background-color: var(--sklearn-color-background);
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
#sk-container-id-2 div.sk-container {
|
| 67 |
-
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
| 68 |
-
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
| 69 |
-
so we also need the `!important` here to be able to override the
|
| 70 |
-
default hidden behavior on the sphinx rendered scikit-learn.org.
|
| 71 |
-
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
| 72 |
-
display: inline-block !important;
|
| 73 |
-
position: relative;
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
#sk-container-id-2 div.sk-text-repr-fallback {
|
| 77 |
-
display: none;
|
| 78 |
-
}
|
| 79 |
-
|
| 80 |
-
div.sk-parallel-item,
|
| 81 |
-
div.sk-serial,
|
| 82 |
-
div.sk-item {
|
| 83 |
-
/* draw centered vertical line to link estimators */
|
| 84 |
-
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
| 85 |
-
background-size: 2px 100%;
|
| 86 |
-
background-repeat: no-repeat;
|
| 87 |
-
background-position: center center;
|
| 88 |
-
}
|
| 89 |
-
|
| 90 |
-
/* Parallel-specific style estimator block */
|
| 91 |
-
|
| 92 |
-
#sk-container-id-2 div.sk-parallel-item::after {
|
| 93 |
-
content: "";
|
| 94 |
-
width: 100%;
|
| 95 |
-
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
| 96 |
-
flex-grow: 1;
|
| 97 |
-
}
|
| 98 |
-
|
| 99 |
-
#sk-container-id-2 div.sk-parallel {
|
| 100 |
-
display: flex;
|
| 101 |
-
align-items: stretch;
|
| 102 |
-
justify-content: center;
|
| 103 |
-
background-color: var(--sklearn-color-background);
|
| 104 |
-
position: relative;
|
| 105 |
-
}
|
| 106 |
-
|
| 107 |
-
#sk-container-id-2 div.sk-parallel-item {
|
| 108 |
-
display: flex;
|
| 109 |
-
flex-direction: column;
|
| 110 |
-
}
|
| 111 |
-
|
| 112 |
-
#sk-container-id-2 div.sk-parallel-item:first-child::after {
|
| 113 |
-
align-self: flex-end;
|
| 114 |
-
width: 50%;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
#sk-container-id-2 div.sk-parallel-item:last-child::after {
|
| 118 |
-
align-self: flex-start;
|
| 119 |
-
width: 50%;
|
| 120 |
-
}
|
| 121 |
-
|
| 122 |
-
#sk-container-id-2 div.sk-parallel-item:only-child::after {
|
| 123 |
-
width: 0;
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
/* Serial-specific style estimator block */
|
| 127 |
-
|
| 128 |
-
#sk-container-id-2 div.sk-serial {
|
| 129 |
-
display: flex;
|
| 130 |
-
flex-direction: column;
|
| 131 |
-
align-items: center;
|
| 132 |
-
background-color: var(--sklearn-color-background);
|
| 133 |
-
padding-right: 1em;
|
| 134 |
-
padding-left: 1em;
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 139 |
-
clickable and can be expanded/collapsed.
|
| 140 |
-
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 141 |
-
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 142 |
-
*/
|
| 143 |
-
|
| 144 |
-
/* Pipeline and ColumnTransformer style (default) */
|
| 145 |
-
|
| 146 |
-
#sk-container-id-2 div.sk-toggleable {
|
| 147 |
-
/* Default theme specific background. It is overwritten whether we have a
|
| 148 |
-
specific estimator or a Pipeline/ColumnTransformer */
|
| 149 |
-
background-color: var(--sklearn-color-background);
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
/* Toggleable label */
|
| 153 |
-
#sk-container-id-2 label.sk-toggleable__label {
|
| 154 |
-
cursor: pointer;
|
| 155 |
-
display: block;
|
| 156 |
-
width: 100%;
|
| 157 |
-
margin-bottom: 0;
|
| 158 |
-
padding: 0.5em;
|
| 159 |
-
box-sizing: border-box;
|
| 160 |
-
text-align: center;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:before {
|
| 164 |
-
/* Arrow on the left of the label */
|
| 165 |
-
content: "▸";
|
| 166 |
-
float: left;
|
| 167 |
-
margin-right: 0.25em;
|
| 168 |
-
color: var(--sklearn-color-icon);
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
|
| 172 |
-
color: var(--sklearn-color-text);
|
| 173 |
-
}
|
| 174 |
-
|
| 175 |
-
/* Toggleable content - dropdown */
|
| 176 |
-
|
| 177 |
-
#sk-container-id-2 div.sk-toggleable__content {
|
| 178 |
-
max-height: 0;
|
| 179 |
-
max-width: 0;
|
| 180 |
-
overflow: hidden;
|
| 181 |
-
text-align: left;
|
| 182 |
-
/* unfitted */
|
| 183 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 184 |
-
}
|
| 185 |
-
|
| 186 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted {
|
| 187 |
-
/* fitted */
|
| 188 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 189 |
-
}
|
| 190 |
-
|
| 191 |
-
#sk-container-id-2 div.sk-toggleable__content pre {
|
| 192 |
-
margin: 0.2em;
|
| 193 |
-
border-radius: 0.25em;
|
| 194 |
-
color: var(--sklearn-color-text);
|
| 195 |
-
/* unfitted */
|
| 196 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
#sk-container-id-2 div.sk-toggleable__content.fitted pre {
|
| 200 |
-
/* unfitted */
|
| 201 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 202 |
-
}
|
| 203 |
-
|
| 204 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
| 205 |
-
/* Expand drop-down */
|
| 206 |
-
max-height: 200px;
|
| 207 |
-
max-width: 100%;
|
| 208 |
-
overflow: auto;
|
| 209 |
-
}
|
| 210 |
-
|
| 211 |
-
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
| 212 |
-
content: "▾";
|
| 213 |
-
}
|
| 214 |
-
|
| 215 |
-
/* Pipeline/ColumnTransformer-specific style */
|
| 216 |
-
|
| 217 |
-
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 218 |
-
color: var(--sklearn-color-text);
|
| 219 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 220 |
-
}
|
| 221 |
-
|
| 222 |
-
#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 223 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 224 |
-
}
|
| 225 |
-
|
| 226 |
-
/* Estimator-specific style */
|
| 227 |
-
|
| 228 |
-
/* Colorize estimator box */
|
| 229 |
-
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 230 |
-
/* unfitted */
|
| 231 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 232 |
-
}
|
| 233 |
-
|
| 234 |
-
#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
| 235 |
-
/* fitted */
|
| 236 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 237 |
-
}
|
| 238 |
-
|
| 239 |
-
#sk-container-id-2 div.sk-label label.sk-toggleable__label,
|
| 240 |
-
#sk-container-id-2 div.sk-label label {
|
| 241 |
-
/* The background is the default theme color */
|
| 242 |
-
color: var(--sklearn-color-text-on-default-background);
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
/* On hover, darken the color of the background */
|
| 246 |
-
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
|
| 247 |
-
color: var(--sklearn-color-text);
|
| 248 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 249 |
-
}
|
| 250 |
-
|
| 251 |
-
/* Label box, darken color on hover, fitted */
|
| 252 |
-
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
| 253 |
-
color: var(--sklearn-color-text);
|
| 254 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 255 |
-
}
|
| 256 |
-
|
| 257 |
-
/* Estimator label */
|
| 258 |
-
|
| 259 |
-
#sk-container-id-2 div.sk-label label {
|
| 260 |
-
font-family: monospace;
|
| 261 |
-
font-weight: bold;
|
| 262 |
-
display: inline-block;
|
| 263 |
-
line-height: 1.2em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
#sk-container-id-2 div.sk-label-container {
|
| 267 |
-
text-align: center;
|
| 268 |
-
}
|
| 269 |
-
|
| 270 |
-
/* Estimator-specific */
|
| 271 |
-
#sk-container-id-2 div.sk-estimator {
|
| 272 |
-
font-family: monospace;
|
| 273 |
-
border: 1px dotted var(--sklearn-color-border-box);
|
| 274 |
-
border-radius: 0.25em;
|
| 275 |
-
box-sizing: border-box;
|
| 276 |
-
margin-bottom: 0.5em;
|
| 277 |
-
/* unfitted */
|
| 278 |
-
background-color: var(--sklearn-color-unfitted-level-0);
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
#sk-container-id-2 div.sk-estimator.fitted {
|
| 282 |
-
/* fitted */
|
| 283 |
-
background-color: var(--sklearn-color-fitted-level-0);
|
| 284 |
-
}
|
| 285 |
-
|
| 286 |
-
/* on hover */
|
| 287 |
-
#sk-container-id-2 div.sk-estimator:hover {
|
| 288 |
-
/* unfitted */
|
| 289 |
-
background-color: var(--sklearn-color-unfitted-level-2);
|
| 290 |
-
}
|
| 291 |
-
|
| 292 |
-
#sk-container-id-2 div.sk-estimator.fitted:hover {
|
| 293 |
-
/* fitted */
|
| 294 |
-
background-color: var(--sklearn-color-fitted-level-2);
|
| 295 |
-
}
|
| 296 |
-
|
| 297 |
-
/* Specification for estimator info (e.g. "i" and "?") */
|
| 298 |
-
|
| 299 |
-
/* Common style for "i" and "?" */
|
| 300 |
-
|
| 301 |
-
.sk-estimator-doc-link,
|
| 302 |
-
a:link.sk-estimator-doc-link,
|
| 303 |
-
a:visited.sk-estimator-doc-link {
|
| 304 |
-
float: right;
|
| 305 |
-
font-size: smaller;
|
| 306 |
-
line-height: 1em;
|
| 307 |
-
font-family: monospace;
|
| 308 |
-
background-color: var(--sklearn-color-background);
|
| 309 |
-
border-radius: 1em;
|
| 310 |
-
height: 1em;
|
| 311 |
-
width: 1em;
|
| 312 |
-
text-decoration: none !important;
|
| 313 |
-
margin-left: 1ex;
|
| 314 |
-
/* unfitted */
|
| 315 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 316 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
.sk-estimator-doc-link.fitted,
|
| 320 |
-
a:link.sk-estimator-doc-link.fitted,
|
| 321 |
-
a:visited.sk-estimator-doc-link.fitted {
|
| 322 |
-
/* fitted */
|
| 323 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 324 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
/* On hover */
|
| 328 |
-
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 329 |
-
.sk-estimator-doc-link:hover,
|
| 330 |
-
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 331 |
-
.sk-estimator-doc-link:hover {
|
| 332 |
-
/* unfitted */
|
| 333 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 334 |
-
color: var(--sklearn-color-background);
|
| 335 |
-
text-decoration: none;
|
| 336 |
-
}
|
| 337 |
-
|
| 338 |
-
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 339 |
-
.sk-estimator-doc-link.fitted:hover,
|
| 340 |
-
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 341 |
-
.sk-estimator-doc-link.fitted:hover {
|
| 342 |
-
/* fitted */
|
| 343 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 344 |
-
color: var(--sklearn-color-background);
|
| 345 |
-
text-decoration: none;
|
| 346 |
-
}
|
| 347 |
-
|
| 348 |
-
/* Span, style for the box shown on hovering the info icon */
|
| 349 |
-
.sk-estimator-doc-link span {
|
| 350 |
-
display: none;
|
| 351 |
-
z-index: 9999;
|
| 352 |
-
position: relative;
|
| 353 |
-
font-weight: normal;
|
| 354 |
-
right: .2ex;
|
| 355 |
-
padding: .5ex;
|
| 356 |
-
margin: .5ex;
|
| 357 |
-
width: min-content;
|
| 358 |
-
min-width: 20ex;
|
| 359 |
-
max-width: 50ex;
|
| 360 |
-
color: var(--sklearn-color-text);
|
| 361 |
-
box-shadow: 2pt 2pt 4pt #999;
|
| 362 |
-
/* unfitted */
|
| 363 |
-
background: var(--sklearn-color-unfitted-level-0);
|
| 364 |
-
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
.sk-estimator-doc-link.fitted span {
|
| 368 |
-
/* fitted */
|
| 369 |
-
background: var(--sklearn-color-fitted-level-0);
|
| 370 |
-
border: var(--sklearn-color-fitted-level-3);
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
.sk-estimator-doc-link:hover span {
|
| 374 |
-
display: block;
|
| 375 |
-
}
|
| 376 |
-
|
| 377 |
-
/* "?"-specific style due to the `<a>` HTML tag */
|
| 378 |
-
|
| 379 |
-
#sk-container-id-2 a.estimator_doc_link {
|
| 380 |
-
float: right;
|
| 381 |
-
font-size: 1rem;
|
| 382 |
-
line-height: 1em;
|
| 383 |
-
font-family: monospace;
|
| 384 |
-
background-color: var(--sklearn-color-background);
|
| 385 |
-
border-radius: 1rem;
|
| 386 |
-
height: 1rem;
|
| 387 |
-
width: 1rem;
|
| 388 |
-
text-decoration: none;
|
| 389 |
-
/* unfitted */
|
| 390 |
-
color: var(--sklearn-color-unfitted-level-1);
|
| 391 |
-
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
| 392 |
-
}
|
| 393 |
-
|
| 394 |
-
#sk-container-id-2 a.estimator_doc_link.fitted {
|
| 395 |
-
/* fitted */
|
| 396 |
-
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
| 397 |
-
color: var(--sklearn-color-fitted-level-1);
|
| 398 |
-
}
|
| 399 |
-
|
| 400 |
-
/* On hover */
|
| 401 |
-
#sk-container-id-2 a.estimator_doc_link:hover {
|
| 402 |
-
/* unfitted */
|
| 403 |
-
background-color: var(--sklearn-color-unfitted-level-3);
|
| 404 |
-
color: var(--sklearn-color-background);
|
| 405 |
-
text-decoration: none;
|
| 406 |
-
}
|
| 407 |
-
|
| 408 |
-
#sk-container-id-2 a.estimator_doc_link.fitted:hover {
|
| 409 |
-
/* fitted */
|
| 410 |
-
background-color: var(--sklearn-color-fitted-level-3);
|
| 411 |
-
}
|
| 412 |
-
</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor(max_depth=34)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(max_depth=34)</pre></div> </div></div></div></div>
|
| 413 |
-
</body>
|
| 414 |
-
</html>
|
| 415 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
env:
|
| 5 |
-
conda: conda.yaml
|
| 6 |
-
virtualenv: python_env.yaml
|
| 7 |
-
loader_module: mlflow.sklearn
|
| 8 |
-
model_path: model.pkl
|
| 9 |
-
predict_fn: predict
|
| 10 |
-
python_version: 3.11.0
|
| 11 |
-
sklearn:
|
| 12 |
-
code: null
|
| 13 |
-
pickled_model: model.pkl
|
| 14 |
-
serialization_format: cloudpickle
|
| 15 |
-
sklearn_version: 1.5.2
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 34769333
|
| 18 |
-
model_uuid: 6d6ca6151f804bf69e8c2ad9a6f0deb8
|
| 19 |
-
run_id: ff9b209269104b37b82bb9564fe96907
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-30 12:24:07.662782'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- cloudpickle==3.0.0
|
| 9 |
-
- numpy==1.26.2
|
| 10 |
-
- pandas==2.2.2
|
| 11 |
-
- psutil==5.9.4
|
| 12 |
-
- scikit-learn==1.5.2
|
| 13 |
-
- scipy==1.11.4
|
| 14 |
-
- typing==3.7.4.3
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/model.pkl
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:6b51c46f274e74ab79fd748cbe4fa01c5216d66f347bd96f71cfedf31015152f
|
| 3 |
-
size 34769333
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/149819317988706962/ff9b209269104b37b82bb9564fe96907/artifacts/model/requirements.txt
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mlflow==2.16.2
|
| 2 |
-
cloudpickle==3.0.0
|
| 3 |
-
numpy==1.26.2
|
| 4 |
-
pandas==2.2.2
|
| 5 |
-
psutil==5.9.4
|
| 6 |
-
scikit-learn==1.5.2
|
| 7 |
-
scipy==1.11.4
|
| 8 |
-
typing==3.7.4.3
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/feature_importance_weight.json
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
{"f0": 30335.0, "f1": 12283.0, "f2": 8903.0, "f3": 6526.0, "f4": 8117.0, "f5": 7541.0, "f6": 6146.0, "f7": 6050.0, "f8": 3123.0, "f9": 3844.0, "f10": 6510.0, "f11": 4622.0, "f12": 3717.0, "f13": 3799.0, "f14": 2906.0, "f15": 3587.0, "f16": 3904.0, "f17": 4225.0, "f18": 3999.0, "f19": 2503.0, "f20": 2764.0, "f21": 4179.0, "f22": 4006.0, "f23": 3549.0, "f24": 3621.0, "f25": 2669.0, "f26": 2954.0, "f27": 3504.0, "f28": 3645.0, "f29": 3696.0, "f30": 2283.0, "f31": 2338.0, "f32": 3725.0}
|
|
|
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/feature_importance_weight.png
DELETED
|
Binary file (31.4 kB)
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
data: model.xgb
|
| 5 |
-
env:
|
| 6 |
-
conda: conda.yaml
|
| 7 |
-
virtualenv: python_env.yaml
|
| 8 |
-
loader_module: mlflow.xgboost
|
| 9 |
-
python_version: 3.11.0
|
| 10 |
-
xgboost:
|
| 11 |
-
code: null
|
| 12 |
-
data: model.xgb
|
| 13 |
-
model_class: xgboost.sklearn.XGBRegressor
|
| 14 |
-
model_format: xgb
|
| 15 |
-
xgb_version: 2.1.1
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 12354728
|
| 18 |
-
model_uuid: ae7825e16b794647905010b3fabdf5ee
|
| 19 |
-
run_id: 2ad059c5d4704ed088a288d572818bcf
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float32", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-29 20:34:46.696536'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- numpy==1.26.2
|
| 9 |
-
- pandas==2.2.2
|
| 10 |
-
- psutil==5.9.4
|
| 11 |
-
- scikit-learn==1.5.2
|
| 12 |
-
- scipy==1.11.4
|
| 13 |
-
- typing==3.7.4.3
|
| 14 |
-
- xgboost==2.1.1
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/model.xgb
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:c823873e6e051da216bde5618c3db8390d1cdc8e1edd143c59272d69548fc05f
|
| 3 |
-
size 12354728
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/2ad059c5d4704ed088a288d572818bcf/artifacts/model/requirements.txt
DELETED
|
@@ -1,8 +0,0 @@
|
|
| 1 |
-
mlflow==2.16.2
|
| 2 |
-
numpy==1.26.2
|
| 3 |
-
pandas==2.2.2
|
| 4 |
-
psutil==5.9.4
|
| 5 |
-
scikit-learn==1.5.2
|
| 6 |
-
scipy==1.11.4
|
| 7 |
-
typing==3.7.4.3
|
| 8 |
-
xgboost==2.1.1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/feature_importance_weight.json
DELETED
|
@@ -1 +0,0 @@
|
|
| 1 |
-
{"f0": 30335.0, "f1": 12283.0, "f2": 8903.0, "f3": 6526.0, "f4": 8117.0, "f5": 7541.0, "f6": 6146.0, "f7": 6050.0, "f8": 3123.0, "f9": 3844.0, "f10": 6510.0, "f11": 4622.0, "f12": 3717.0, "f13": 3799.0, "f14": 2906.0, "f15": 3587.0, "f16": 3904.0, "f17": 4225.0, "f18": 3999.0, "f19": 2503.0, "f20": 2764.0, "f21": 4179.0, "f22": 4006.0, "f23": 3549.0, "f24": 3621.0, "f25": 2669.0, "f26": 2954.0, "f27": 3504.0, "f28": 3645.0, "f29": 3696.0, "f30": 2283.0, "f31": 2338.0, "f32": 3725.0}
|
|
|
|
|
|
mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/feature_importance_weight.png
DELETED
|
Binary file (31.4 kB)
|
|
|
mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/MLmodel
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
artifact_path: model
|
| 2 |
-
flavors:
|
| 3 |
-
python_function:
|
| 4 |
-
data: model.xgb
|
| 5 |
-
env:
|
| 6 |
-
conda: conda.yaml
|
| 7 |
-
virtualenv: python_env.yaml
|
| 8 |
-
loader_module: mlflow.xgboost
|
| 9 |
-
python_version: 3.11.0
|
| 10 |
-
xgboost:
|
| 11 |
-
code: null
|
| 12 |
-
data: model.xgb
|
| 13 |
-
model_class: xgboost.sklearn.XGBRegressor
|
| 14 |
-
model_format: xgb
|
| 15 |
-
xgb_version: 2.1.1
|
| 16 |
-
mlflow_version: 2.16.2
|
| 17 |
-
model_size_bytes: 12354728
|
| 18 |
-
model_uuid: 4edaf6d4ff8a40fc9b0964a865424d34
|
| 19 |
-
run_id: 32599368741e4784aaa387a8ec350b73
|
| 20 |
-
signature:
|
| 21 |
-
inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 33]}}]'
|
| 22 |
-
outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float32", "shape": [-1,
|
| 23 |
-
6]}}]'
|
| 24 |
-
params: null
|
| 25 |
-
utc_time_created: '2024-09-29 21:39:08.545139'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/conda.yaml
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
channels:
|
| 2 |
-
- conda-forge
|
| 3 |
-
dependencies:
|
| 4 |
-
- python=3.11.0
|
| 5 |
-
- pip<=24.2
|
| 6 |
-
- pip:
|
| 7 |
-
- mlflow==2.16.2
|
| 8 |
-
- numpy==1.26.2
|
| 9 |
-
- pandas==2.2.2
|
| 10 |
-
- psutil==5.9.4
|
| 11 |
-
- scikit-learn==1.5.2
|
| 12 |
-
- scipy==1.11.4
|
| 13 |
-
- typing==3.7.4.3
|
| 14 |
-
- xgboost==2.1.1
|
| 15 |
-
name: mlflow-env
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/model.xgb
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:c823873e6e051da216bde5618c3db8390d1cdc8e1edd143c59272d69548fc05f
|
| 3 |
-
size 12354728
|
|
|
|
|
|
|
|
|
|
|
|
mlartifacts/674375719018272828/32599368741e4784aaa387a8ec350b73/artifacts/model/python_env.yaml
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
python: 3.11.0
|
| 2 |
-
build_dependencies:
|
| 3 |
-
- pip==24.2
|
| 4 |
-
- setuptools==65.5.0
|
| 5 |
-
- wheel==0.41.2
|
| 6 |
-
dependencies:
|
| 7 |
-
- -r requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|