def __init__(self,
normalize=False,
balance=False,
tweet_threshold=0,
score=False,
dump_model=True):
"""
Import or train the regression model
"""
self.model = RegressionModel()
if not self.model.load():
training_set, testing_set = RegressionModel.load_datasets(
balance=balance, viral_threshold=tweet_threshold)
if ViralityPrediction.CLASSIFICATION == True:
training_set = self.model.normaliseFeats(training_set)
testing_set = self.model.normaliseFeats(testing_set)
self.model.trainClassifier(training_set, normalize=normalize)
if score:
self.model.scoreClassifier(testing_set)
else:
self.model.trainRegression(training_set, normalize=normalize)
if score:
self.model.scoreRegression(testing_set)
if dump_model:
self.model.dump()
import pandas as pd
from regression import RegressionModel
model = RegressionModel()
print(model.predict())
print(model.predict(use_macro=False))
# year_prior = row['year']-years
# home_match = self.history_df.loc[self.history_df['home_id'] == row['home_id']]
# row_prior = home_match.loc[home_match['year'] == year_prior]
# # Break if not found
# if len(home_match)<2 or len(row_prior) < 1:
# break
# # Create new row with home data, prev_year, prev_value, prev_value
# row_prior = row_prior.drop("home_id", axis=1)
# row_prior = row_prior.drop("year", axis=1)
# row_prior = row_prior.values.tolist()[0]
# row_prior = [row['year'], row['value']] + row_prior
# # Add row to DataFrame
# row_series = pd.Series(row_prior, index=pred_df.columns)
# pred_df = pred_df.append(row_series, ignore_index=True)
