Chapter 19
duration prediction original
NotebookPython 3 (ipykernel)28 cells
Originally duration-prediction.ipynb from Module 2.
In [1]python · cell 2
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!python -VOutput
Python 3.9.12
In [2]python · cell 3
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import pandas as pdIn [3]python · cell 4
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import pickleIn [4]python · cell 5
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import seaborn as sns
import matplotlib.pyplot as pltIn [5]python · cell 6
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from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_errorIn [44]python · cell 7
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import mlflow
mlflow.set_tracking_uri("sqlite:///mlflow.db")
mlflow.set_experiment("nyc-taxi-experiment")Output
<Experiment: artifact_location='./mlruns/1', experiment_id='1', lifecycle_stage='active', name='nyc-taxi-experiment', tags={}>In [7]python · cell 8
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def read_dataframe(filename):
df = pd.read_csv(filename)
df.lpep_dropoff_datetime = pd.to_datetime(df.lpep_dropoff_datetime)
df.lpep_pickup_datetime = pd.to_datetime(df.lpep_pickup_datetime)
df['duration'] = df.lpep_dropoff_datetime - df.lpep_pickup_datetime
df.duration = df.duration.apply(lambda td: td.total_seconds() / 60)
df = df[(df.duration >= 1) & (df.duration <= 60)]
categorical = ['PULocationID', 'DOLocationID']
df[categorical] = df[categorical].astype(str)
return dfIn [8]python · cell 9
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df_train = read_dataframe('./data/green_tripdata_2021-01.csv')
df_val = read_dataframe('./data/green_tripdata_2021-02.csv')Output
/var/folders/42/f9s_rgk15078ym2w50_xtc180000gq/T/ipykernel_4832/3970424726.py:2: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False. df = pd.read_csv(filename)
In [9]python · cell 10
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len(df_train), len(df_val)Output
(73908, 61921)
In [10]python · cell 11
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df_train['PU_DO'] = df_train['PULocationID'] + '_' + df_train['DOLocationID']
df_val['PU_DO'] = df_val['PULocationID'] + '_' + df_val['DOLocationID']In [11]python · cell 12
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categorical = ['PU_DO'] #'PULocationID', 'DOLocationID']
numerical = ['trip_distance']
dv = DictVectorizer()
train_dicts = df_train[categorical + numerical].to_dict(orient='records')
X_train = dv.fit_transform(train_dicts)
val_dicts = df_val[categorical + numerical].to_dict(orient='records')
X_val = dv.transform(val_dicts)In [12]python · cell 13
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target = 'duration'
y_train = df_train[target].values
y_val = df_val[target].valuesIn [13]python · cell 14
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lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_val)
mean_squared_error(y_val, y_pred, squared=False)Output
7.758715210382775
In [14]python · cell 15
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with open('models/lin_reg.bin', 'wb') as f_out:
pickle.dump((dv, lr), f_out)In [47]python · cell 16
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with mlflow.start_run():
mlflow.set_tag("developer", "cristian")
mlflow.log_param("train-data-path", "./data/green_tripdata_2021-01.csv")
mlflow.log_param("valid-data-path", "./data/green_tripdata_2021-02.csv")
alpha = 0.1
mlflow.log_param("alpha", alpha)
lr = Lasso(alpha)
lr.fit(X_train, y_train)
y_pred = lr.predict(X_val)
rmse = mean_squared_error(y_val, y_pred, squared=False)
mlflow.log_metric("rmse", rmse)
mlflow.log_artifact(local_path="models/lin_reg.bin", artifact_path="models_pickle")In [ ]python · cell 17
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In [15]python · cell 18
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import xgboost as xgbIn [16]python · cell 19
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from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from hyperopt.pyll import scopeIn [17]python · cell 20
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train = xgb.DMatrix(X_train, label=y_train)
valid = xgb.DMatrix(X_val, label=y_val)In [23]python · cell 21
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def objective(params):
with mlflow.start_run():
mlflow.set_tag("model", "xgboost")
mlflow.log_params(params)
booster = xgb.train(
params=params,
dtrain=train,
num_boost_round=1000,
evals=[(valid, 'validation')],
early_stopping_rounds=50
)
y_pred = booster.predict(valid)
rmse = mean_squared_error(y_val, y_pred, squared=False)
mlflow.log_metric("rmse", rmse)
return {'loss': rmse, 'status': STATUS_OK}In [24]python · cell 22
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search_space = {
'max_depth': scope.int(hp.quniform('max_depth', 4, 100, 1)),
'learning_rate': hp.loguniform('learning_rate', -3, 0),
'reg_alpha': hp.loguniform('reg_alpha', -5, -1),
'reg_lambda': hp.loguniform('reg_lambda', -6, -1),
'min_child_weight': hp.loguniform('min_child_weight', -1, 3),
'objective': 'reg:linear',
'seed': 42
}
best_result = fmin(
fn=objective,
space=search_space,
algo=tpe.suggest,
max_evals=50,
trials=Trials()
)Output
[12:38:06] WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-11.0-arm64-3.8/xgboost/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror. 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WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-11.0-arm64-3.8/xgboost/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror. 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In [18]python · cell 23
python
mlflow.xgboost.autolog(disable=True)In [21]python · cell 24
python
with mlflow.start_run():
train = xgb.DMatrix(X_train, label=y_train)
valid = xgb.DMatrix(X_val, label=y_val)
best_params = {
'learning_rate': 0.09585355369315604,
'max_depth': 30,
'min_child_weight': 1.060597050922164,
'objective': 'reg:linear',
'reg_alpha': 0.018060244040060163,
'reg_lambda': 0.011658731377413597,
'seed': 42
}
mlflow.log_params(best_params)
booster = xgb.train(
params=best_params,
dtrain=train,
num_boost_round=1000,
evals=[(valid, 'validation')],
early_stopping_rounds=50
)
y_pred = booster.predict(valid)
rmse = mean_squared_error(y_val, y_pred, squared=False)
mlflow.log_metric("rmse", rmse)
with open("models/preprocessor.b", "wb") as f_out:
pickle.dump(dv, f_out)
mlflow.log_artifact("models/preprocessor.b", artifact_path="preprocessor")
mlflow.xgboost.log_model(booster, artifact_path="models_mlflow")Output
[11:40:58] WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-11.0-arm64-3.8/xgboost/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror. 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validation-rmse:6.30429 [994] validation-rmse:6.30429 [995] validation-rmse:6.30412 [996] validation-rmse:6.30410 [997] validation-rmse:6.30407 [998] validation-rmse:6.30405 [999] validation-rmse:6.30398
In [ ]python · cell 25
python
In [23]python · cell 26
python
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor
from sklearn.svm import LinearSVR
mlflow.sklearn.autolog()
for model_class in (RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, LinearSVR):
with mlflow.start_run():
mlflow.log_param("train-data-path", "./data/green_tripdata_2021-01.csv")
mlflow.log_param("valid-data-path", "./data/green_tripdata_2021-02.csv")
mlflow.log_artifact("models/preprocessor.b", artifact_path="preprocessor")
mlmodel = model_class()
mlmodel.fit(X_train, y_train)
y_pred = mlmodel.predict(X_val)
rmse = mean_squared_error(y_val, y_pred, squared=False)
mlflow.log_metric("rmse", rmse)
Output
/Users/cristian.martinez/miniconda3/envs/exp-tracking-env/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
In [ ]python · cell 27
python
In [ ]python · cell 28
python