def get_df(cols, load_from_temp, temp_path):
weather_df = load_weather_df(CONFIG.preprocessed_meteo_path_complete)
if not load_from_temp:
logger.info('Loading train Dataframe...')
train_df = load_train_df(CONFIG.preprocessed_train_path_means)
logger.info('Loading weather Dataframe...')
logger.info('Creating features...')
ff = FeatureFactory(train_df, weather_df)
for col in cols:
logger.info('Creating %s feature...' % col)
ff(col)
if 'ASS_ASSIGNMENT' not in cols:
cols = ['ASS_ASSIGNMENT'] + cols
if 'DATE' not in cols:
cols = ['DATE'] + cols
if 'CSPL_RECEIVED_CALLS' not in cols:
cols += ['CSPL_RECEIVED_CALLS']
logger.info('Selecting features...')
ff.select_features(cols)
train_df = ff.X
if temp_path is not None:
train_df.to_csv(temp_path)
else:
assert temp_path is not None
logger.info('Loading train Dataframe...')
train_df = pd.read_csv(temp_path, encoding='latin-1', index_col=0, parse_dates=['DATE'])
weather_df.reset_index(inplace=True)
return train_df, weather_df
def get_holidays():
df = load_train_df(CONFIG.preprocessed_train_path)
# weather_df = load_weather_df(CONFIG.preprocessed_meteo_path)
ff = FeatureFactory(df, weather_df=None)
ff('WEEK_NUMBER')
# ff('NUMB_FROZEN_DEPT')
# ff('NUMB_WET_DEPT')
df = ff.X
for assignment in CONFIG.submission_assignments:
plt.plot(df[df["ASS_ASSIGNMENT"] == assignment].groupby('WEEK_NUMBER')['CSPL_RECEIVED_CALLS'].mean().reset_index())
plt.savefig(os.path.join(visualization_path, "holidays_absolute_values.jpg"))
def complete_data_with_zeros(in_path, out_path=None):
logger.debug('Loading Dataframe...')
train_df = load_train_df(in_path)
logger.debug('Generating empty Dataframe...')
dates = sorted(set(train_df['DATE']))
zero_date_df = pd.DataFrame({'DATE': dates})
zero_date_df['ZERO'] = 0
zero_ass_df = pd.DataFrame({'ASS_ASSIGNMENT': CONFIG.submission_assignments})
zero_ass_df['ZERO'] = 0
zero_df = zero_date_df.merge(zero_ass_df, how='left', on='ZERO')
logger.debug('Completing Dataframe...')
train_df = zero_df.merge(train_df, how='left', on=['DATE', 'ASS_ASSIGNMENT'])
train_df['CSPL_RECEIVED_CALLS'].fillna(0, inplace=True)
train_df.drop('ZERO', axis=1, inplace=True)
logger.debug('Saving Dataframe...')
if out_path is not None:
train_df.to_csv(out_path)
return train_df
def run(train_or_meteo=None, train_cols=None, meteo_cols=None, verbose=0):
if train_or_meteo is None or train_or_meteo == 'train':
parse_train_as_df(CONFIG.raw_train_path, CONFIG.preprocessed_train_path, useful_cols=train_cols,
verbose=verbose)
logger.info('Saved train in csv file.')
complete_data_with_zeros(CONFIG.preprocessed_train_path, CONFIG.preprocessed_train_path_zeros)
logger.info('Saved completed train in csv file.')
complete_with_means(load_train_df(CONFIG.preprocessed_train_path_zeros), CONFIG.means_path)
logger.info('Saved train with means in csv file.')
if train_or_meteo is None or train_or_meteo == 'meteo':
print('Reading meteo file 1...')
df1 = parse_meteo_as_df(CONFIG.raw_meteo_path1)
print('Reading meteo file 2...')
df2 = parse_meteo_as_df(CONFIG.raw_meteo_path2)
print('Concatenating meteo files...')
df = pd.concat([df1, df2])
print('Meteo files concatenated. Running preprocessing...')
# Number of departments where it has rained and where it has frozen
print('Meteo1...')
preprocess_meteo1(df)
# Average amount of rain and average lowest temperatures in each department.
print('Meteo2...')
preprocess_meteo2(df)
# Booleans for each department where it has rained and where it has frozen
print('Meteo3...')
preprocess_meteo3(df)
# Booleans for each department where average amount of rain is above 1mm and where it has frozen.
print('Meteo4...')
preprocess_meteo4(df)
return df
def compare_calls(scale, out_path, assignments=None, datetime=None):
"""
Plot the number of calls to compare them.
Parameters
==========
scale: 'DATETIME', 'DAY', 'WEEK' or 'YEAR', calls are averaged on all smaller scales,
and plotted for larger scales.
out_path: str, folder in which figures should be saved.
assignments: str or list of str, assignments to take into account.
None to take all columns into account.
datetime: if 'DATETIME', the datetime to filter on
Example
=======
Week comparison: For each day of the week, take the average number of calls, then compare for each week of the year.
"""
assert scale in ['DATETIME', 'DAY', 'WEEK', 'YEAR']
if assignments is not None:
if isinstance(assignments, str):
assignments = [assignments]
assert not set(assignments).difference(CONFIG.submission_assignments)
else:
assignments = CONFIG.submission_assignments
df = load_train_df(CONFIG.preprocessed_train_path)
df = df[df["ASS_ASSIGNMENT"].isin(assignments)]
# if remove_days_off:
# df = df[df["DAY_OFF"] == 0]
# df.drop("DAY_OFF", axis=1, inplace=True)
ff = FeatureFactory(df)
for column in ["WEEK_NUMBER", "WEEK_DAY", "TIME"]:
ff(column)
df = ff.X
if scale == 'DATETIME':
assert datetime is not None
df = df[ff("WEEK_DAY") == datetime.isoweekday()]
df = df[df['TIME'] == datetime.hour + float(datetime.minute)/60]
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment].reset_index()
plt.plot(df_assignment['CSPL_RECEIVED_CALLS'])
weather_df = load_weather_df(CONFIG.preprocessed_meteo_path)
good_days = [d for d in weather_df.index if d.isoweekday() == datetime.isoweekday()]
weather_df = weather_df.loc[good_days, :].reset_index()
plt.plot(weather_df['NUMB_FROZEN_DEPT'])
plt.plot(weather_df['NUMB_WET_DEPT'])
plt.savefig(os.path.join(out_path, scale+".jpg"))
if scale == 'DAY':
grouped = df.groupby(["ASS_ASSIGNMENT", "WEEK_NUMBER", "WEEK_DAY", "TIME"])
df = grouped["CSPL_RECEIVED_CALLS"].sum().reset_index()
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment]
for day in range(366):
df_day = df_assignment[df_assignment['WEEK_NUMBER'] == int(day/7 + 1)]
df_day = df_day[df_day['WEEK_DAY'] == day % 7]
plt.plot(df_day['TIME'], df_day["CSPL_RECEIVED_CALLS"])
plt.savefig(os.path.join(out_path, scale+"_"+assignment+".jpg"))
plt.clf()
if scale == 'WEEK':
grouped = df.groupby(["ASS_ASSIGNMENT", "WEEK_NUMBER", "WEEK_DAY"])
df = grouped["CSPL_RECEIVED_CALLS"].mean().reset_index()
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment]
for week_number in range(53):
df_week = df_assignment[df_assignment['WEEK_NUMBER'] == week_number]
plt.plot(df_week['WEEK_DAY'], df_week["CSPL_RECEIVED_CALLS"])
plt.savefig(os.path.join(out_path, scale+"_"+assignment+".jpg"))
plt.clf()
if scale == 'YEAR':
grouped = df.groupby(["ASS_ASSIGNMENT", "WEEK_NUMBER"])
df = grouped["CSPL_RECEIVED_CALLS"].mean().reset_index()
for assignment in assignments:
print(assignment)
df_assignment = df[df['ASS_ASSIGNMENT'] == assignment]
plt.plot(df_assignment['WEEK_DAY'], df_assignment["CSPL_RECEIVED_CALLS"])
# plt.axis([0, 52, 0, 50])
plt.savefig(os.path.join(out_path, scale+"_absolute_values.jpg"))
plt.clf()
import preprocessing
import learning
from utils import load_submission, load_train_df
from configuration import CONFIG
if __name__ == "__main__":
# Create train and meteo preprocessed files
preprocessing.run()
# Define a model
from sklearn.neighbors import KNeighborsRegressor
_df = load_train_df(CONFIG.preprocessed_train_path)
_submission_df = load_submission()
_estimator = KNeighborsRegressor(n_neighbors=4, weights='distance')
# estimator = LogisticRegression()
_scoring = 'mean_squared_error'
_k_fold = 3
_n_jobs = 3
_verbose = 0
_fit_params = None
_cols = ["YEAR", "WEEK_NUMBER", "WEEK_DAY", "TIME"]
_weights = [1, 1, 1, 0.1]
# Test the model
print(learning.cross_val_score(_estimator, _cols, _k_fold, _weights, _scoring, _n_jobs, _verbose, _fit_params,
chunksize=100000))
# Create the corresponding submission file
learning.create_submission_file(_estimator, _cols, weights=_weights)