def main(in_dir, out_dir):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
in_file = in_dir + '/train.csv'
print "reading datafile: " + str(in_file)
df = pd.read_table(in_file, sep=',', header=0, parse_dates=['datetime'])
df['dow'] = df['datetime'].apply(lambda x: x.weekday())
X, enc, scalar = bc.prep_data(df)
y = df[['count']].values
X_train, X_test, y_train, y_test = sl.cross_validation.train_test_split(X, y)
# clf = linear_model.LinearRegression()
# clf = sl.tree.DecisionTreeRegressor()
# clf = ensemble.GradientBoostingClassifier()
# clf = ensemble.GradientBoostingRegressor()
clf = ensemble.RandomForestRegressor()
param_grid = {'n_estimators': [5,10,20,50], 'max_features': (None,0.75,0.50,0.25)}
rmsle_scorer = sl.metrics.make_scorer(bc.score_func, greater_is_better=False)
# train the rental model
print ('training')
srch = sl.grid_search.GridSearchCV(clf, param_grid, rmsle_scorer)
srch.fit(X_train, y_train.ravel())
clf = srch.best_estimator_
print('clf stats: best_score=%f best_params=%s' % (srch.best_score_, srch.best_params_) )
zc = clf.predict(X_test)
zc[zc<0] = 0
print ('clf Xtrain RMSLE: ' + str(bc.score_func(y_test, zc)))
# full prediction + addition
z = clf.predict(X)
print ('clf Xt RMSLE: ' + str(bc.score_func(y, z)))
##############
# run model against test dataset
test_file = in_dir + '/test.csv'
print "reading test datafile: " + str(test_file)
df_test = pd.read_table(test_file, sep=',',header=0, parse_dates=['datetime'])
df_test['dow'] = df_test['datetime'].apply(lambda x: x.weekday())
Xtest, enc, scalar = bc.prep_data(df_test, enc, scalar)
# full prediction + addition
zc = clf.predict(Xtest)
zc[zc<0] = 0
df_test['count'] = zc
df_test[['datetime','count']].to_csv(out_dir + '/submission.csv', sep=',', header=True, index=False)
def main(in_dir, out_dir):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
in_file = in_dir + '/train.csv'
print "reading datafile: " + str(in_file)
df = pd.read_table(in_file, sep=',', header=0, parse_dates=['datetime'])
df['dow'] = df['datetime'].apply(lambda x: x.weekday())
X, enc, scalar = bc.prep_data(df)
# output prepped file for visual review
# X.to_csv(out_dir + '/X.csv', sep=',', header=True, index=False)
y = df[['count']].values
Xcorr = pd.concat([X, df[['count']]], axis=1)
Ycorr = Xcorr.corr()
# output prepped file for visual review
Ycorr.to_csv(out_dir + '/Ycorr.csv', sep=',', header=True, index=False)
# strip X down to minimal columns based on pearson coef
# cols = [0,1,2,3,7,9,12,14,17,18,20,21,32,33,34,35,36,37,38,40,48,49,50,51,55]
# Xmini = X[cols]
# print("shrunk X from " + str(X.columns) + " to " + str(Xmini.columns))
# X = Xmini
X_train, X_test, y_train, y_test = sl.cross_validation.train_test_split(X, y)
# clf = linear_model.LinearRegression()
# clf = sl.tree.DecisionTreeRegressor()
# clf = ensemble.GradientBoostingClassifier()
# clf = ensemble.GradientBoostingRegressor(verbose=1)
# param_grid = {'loss':('ls','lad','huber','quantile'), 'n_estimators':[50,100,500], 'max_features':(None,'auto'), 'max_depth':(10,20,40)}
clf = ensemble.RandomForestRegressor()
param_grid = {'n_estimators': [5,10,20,50,100,500,1000], 'max_features': (None,0.75,0.50,0.25)}
rmsle_scorer = sl.metrics.make_scorer(bc.score_func, greater_is_better=False)
# train the rental model
print ('training')
srch = sl.grid_search.GridSearchCV(clf, param_grid, scoring=rmsle_scorer, verbose=1)
srch.fit(X_train, y_train.ravel())
clf = srch.best_estimator_
print('clf stats: best_score=%f best_params=%s' % (srch.best_score_, srch.best_params_) )
# clf = ensemble.RandomForestRegressor(n_estimators = 500, verbose = 1, n_jobs=10)
# clf = linear_model.LinearRegression()
# clf = ensemble.GradientBoostingRegressor(n_estimators=500, verbose=1, loss='huber', max_depth=24)
clf.fit(X_train, y_train.ravel())
zc = clf.predict(X_test)
print("number rows < 0 = " + str(len(zc[zc<0])))
zc[zc<0] = 0
print ('clf CV RMSLE: ' + str(bc.score_func(y_test, zc)))
# full prediction + addition
# z = clf.predict(X)
# print ('clf Xt RMSLE: ' + str(bc.score_func(y, z)))
##############
# run model against test dataset
test_file = in_dir + '/test.csv'
print "reading test datafile: " + str(test_file)
df_test = pd.read_table(test_file, sep=',',header=0, parse_dates=['datetime'])
df_test['dow'] = df_test['datetime'].apply(lambda x: x.weekday())
Xtest, enc, scalar = bc.prep_data(df_test, enc, scalar)
# Xtest = Xtest[cols]
# full prediction + addition
zc = clf.predict(Xtest)
zc[zc<0] = 0
df_test['count'] = zc
df_test[['datetime','count']].to_csv(out_dir + '/submission.csv', sep=',', header=True, index=False)
def main(in_dir, out_dir):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
in_file = in_dir + "/train.csv"
print "reading datafile: " + str(in_file)
# names=['datetime','season','holiday','workingday','weather','temp','atemp','humidity','windspeed','casual','registered','count']
df = pd.read_table(in_file, sep=",", header=0, parse_dates=["datetime"])
df["dow"] = df["datetime"].apply(lambda x: x.weekday())
# prepare the feature data
X, enc, scalar = bc.prep_data(df)
X.to_csv(out_dir + "/scratch.csv", sep=",", header=True, index=False)
# extract the labels for the casual and reserved bike sets
yc = df[["casual"]].values
yr = df[["registered"]].values
# create training and test data for casual and reserved data
Xc_train, Xc_test, yc_train, yc_test = sl.cross_validation.train_test_split(X, yc)
Xr_train, Xr_test, yr_train, yr_test = sl.cross_validation.train_test_split(X, yr)
# try out a bunch of different models
# RMLSE score=0.993142954278 (vs. test 15.57659)
# clfc = linear_model.LinearRegression()
# clfr = linear_model.LinearRegression()
# param_grid = {}
# RMLSE score=0.251551972408 (vs. test 0.59778)
# with DOW RMLSE score=0.252605615098 (vs. .59090)
# clfc = sl.tree.DecisionTreeRegressor()
# clfr = sl.tree.DecisionTreeRegressor()
# param_grid = {'max_features':(None,'auto','sqrt')}
# RMLSE score=0.226339353755 (vs. test 0.57022
# clfc = sl.tree.ExtraTreeRegressor()
# clfr = sl.tree.ExtraTreeRegressor()
# param_grid = {'max_features':(None,'auto','sqrt')}
# RMLSE score=1.31458932334
# clfc = sl.svm.SVR()
# clfr = sl.svm.SVR()
# param_grid = {'C':[.01,.1,1,10], 'epsilon':[.01,.1,1], 'kernel': ('rbf','linear','poly','sigmoid')}
# RMLSE score=0.983135646219
# clfc = ensemble.GradientBoostingRegressor()
# clfr = ensemble.GradientBoostingRegressor()
# param_grid = {'loss':('ls','lad','huber','quantile'), 'n_estimators':[50,100,200,500], 'max_features':(None,'auto','sqrt')}
# RMLSE score=0.316815997324 (vs. test 0.54892)
# with params RMLSE score=0.313294668232 (vs. test 0.57309)
# with DOW RMLSE score=0.307161680679 (vs. test 0.55184)
clfc = ensemble.RandomForestRegressor()
clfr = ensemble.RandomForestRegressor()
param_grid = {"n_estimators": [5, 10, 20, 50], "max_features": (None, 0.75, 0.50, 0.25)}
# turn my RMSLE method into a scoring func for use in GridSearch
rmsle_scorer = sl.metrics.make_scorer(bc.score_func, greater_is_better=False)
# train the casual rental model
print ("training 1")
srch = sl.grid_search.GridSearchCV(clfc, param_grid, rmsle_scorer)
srch.fit(Xc_train, yc_train.ravel())
clfc = srch.best_estimator_
print ("clfc stats: best_score=%f best_params=%s" % (srch.best_score_, srch.best_params_))
# re-fit with entire dataset
clfc.fit(X, yc.ravel())
# score this model for fun
zc = clfc.predict(Xc_test)
zc[zc < 0] = 0
print ("clf Xc rmsle: " + str(bc.score_func(yc_test.ravel(), zc.ravel())))
# train the reserved rental model
print ("training 2")
srch = sl.grid_search.GridSearchCV(clfr, param_grid, rmsle_scorer)
srch.fit(Xr_train, yr_train.ravel())
clfr = srch.best_estimator_
print ("clfr stats: best_score=%f best_params=%s" % (srch.best_score_, srch.best_params_))
# re-fit with entire dataset
clfr.fit(X, yr.ravel())
# score this model for fun
zr = clfr.predict(Xr_test)
zr[zr < 0] = 0
print ("clf Xr rmsle: " + str(bc.score_func(yr_test, zr)))
# now add the two predictions and score the combined model
zc = clfc.predict(X)
zc[zc < 0] = 0
zr = clfr.predict(X)
zr[zr < 0] = 0
zt = zc + zr
yt = df[["count"]].values
score = bc.score_func(yt, zt)
print ("RMLSE score=" + str(score))
df["casual_pred"] = zc
df["registered_pred"] = zr
df["count_pred"] = zt
df.to_csv(out_dir + "/train_xval_preds.csv", sep=",", header=True, index=False)
###########################
# apply trained models to test set
test_file = in_dir + "/test.csv"
print "reading test datafile: " + str(test_file)
df_test = pd.read_table(test_file, sep=",", header=0, parse_dates=["datetime"])
df_test["dow"] = df_test["datetime"].apply(lambda x: x.weekday())
Xtest, enc, scalar = bc.prep_data(df_test, enc, scalar)
zc = clfc.predict(Xtest)
zc[zc < 0] = 0
zr = clfr.predict(Xtest)
zr[zr < 0] = 0
zt = zc + zr
df_test["count"] = zt
df_test[["datetime", "count"]].to_csv(out_dir + "/submission.csv", sep=",", header=True, index=False)
