def __init__(self,data,forecastMessure,p,d,q):
Modelling.__init__(self,data,forecastMessure)
self.data=data
self.p=p
self.d=d
self.q=q
self.forecastMessure=forecastMessure
def getFlaks():
# columns values to allow for user selection
model = Modelling()
df, user_input_list = model.data_preparation()
return render_template('main.html', selections=user_input_list)
def __init__(self, data, forecastMessure, seasonal_periods, WStRMSEOpt,
WMAPEOpt, product):
Modelling.__init__(self, data, forecastMessure)
self.data = data
self.forecastMessure = forecastMessure
self.seasonal_periods = seasonal_periods
self.WStRMSEOpt = WStRMSEOpt
self.WMAPEOpt = WMAPEOpt
self.product = product
def fit(args):
"fit preprocessor and model"
data = load_data(DATA_NAME, samp_size=10000, all_=False)
prep = ChrunPrep()
X = prep.fit_transform(data)
y = prep.create_labels(data)
classifier = Modelling(model=args.model_type)
classifier.fit(X, y)
class TestModelling(unittest.TestCase):
"""
Test the Modelling class.
"""
def setUp(self):
self.description = pd.read_csv("docs/description.csv")
self.description_1000 = pd.read_csv("docs/description_1000.csv")
self.mdl = Modelling(self.description)
def test_run_lda(self):
self.mdl.run_lda(10, 100, 10)
""" def test_run_nmf(self):
def predict(args):
"perdict on a given dataset"
if not args.private_file:
data = load_data(DATA_NAME, samp_size=100000, all_=False)
else:
data = load_data(args.private_file)
prep = ChrunPrep()
X, index = prep.transform(data)
classifier = Modelling(args.model_type)
preds = classifier.predict(X)
maybe_mkdir(args.out_path)
out_path = os.path.join(args.out_path, "preds.csv")
pd.Series(preds, index=index).to_csv(out_path, sep=";")
def main():
with open('./data/fm_2000-2019.pkl', 'rb') as handle:
df = pickle.load(handle)
start_game = 30
end_game = 82
#vegas_years = ['2013-14','2014-15','2015-16','2016-17','2017-18','2018-19']
vegas_years = ['2018-19']
first_feature = 'gp_all_0_a'
model_type = 'bayes-normal'
hp_dict = {'alpha': .05}
feature_classes = 'all' #['e-off-rating','e-def-rating','e-pace']
thresh = .6
period = 2
trace_samp = 5000
burn_in = 2000
post_samp = 1000
chains = 4
cores = 1
my_model = Modelling(period=period,model_type=model_type,feature_classes=feature_classes,remove_features=[]\
,restrict_features=[],hp_dict=hp_dict,normalize=False,trace_samp=trace_samp,burn_in=burn_in,post_samp=post_samp,
chains=chains,cores=cores)
vals, mean, var = cross_validate(df,
my_model,
start_game,
end_game,
thresh,
vegas_years,
first_feature,
normalize=False,
ppc=True)
print(mean, var)
def catboost_model(self,
X_train,
y_train,
X_val,
y_val,
cv_type='gridsearch'):
CatBoostClassifier_param = {
"iterations": [100], #[100, 1150, 200, 300],
"learning_rate": [0.08, 0.09], # [0.01, 0.03, 0.1, 0.3, 0.5, 1]
"max_depth": [5], #[3,5,8]
"l2_leaf_reg": [5], #[2,10,15]
}
model = Modelling().best_model_fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
clf=CatBoostClassifier(
verbose=0,
loss_function='Logloss',
random_seed=RANDOM_SEED,
),
param=CatBoostClassifier_param,
cv_type=cv_type)
return model
def lgb_model(self, X_train, y_train, X_val, y_val, cv_type='gridsearch'):
LGBMClassifier_param = {
"learning_rate": [0.2, 0.3],
"num_leaves": [10],
"max_depth": [7, 8],
"feature_fraction": [0.4, 0.6],
"lambda": [0.3, 0.4],
"boosting": ['gbdt', 'dart'],
"num_boost_round": [100, 120],
# "min_gain_to_split": [],
# "max_cat_group": [],
# "bagging_fraction": [],
# "min_data_in_leaf": [],
}
model = Modelling().best_model_fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
clf=LGBMClassifier(
application='binary',
metric='binary_logloss',
save_binary=True),
param=LGBMClassifier_param,
cv_type=cv_type)
return model
def __init__(self,
data,
forecastMessure,
hidden_layers=[20, 15],
activation_functions=['relu', 'relu'],
optimizer=SGD(),
loss='mean_absolute_error'):
Modelling.__init__(self, df, forecastMessure)
self.data = data
self.forecastMessure = forecastMessure
self.hidden_layers = hidden_layers
self.activation_functions = activation_functions
self.optimizer = optimizer
self.loss = loss
if len(self.hidden_layers) != len(self.activation_functions):
raise Exception(
"hidden_layers size must match activation_functions size")
def postFlaks():
# get user's inputs
user_CRIM = request.form.get("CRIM", False)
user_ZN = request.form.get("ZN", False)
user_INDUS = request.form.get("INDUS", False)
user_CHAS = request.form.get("CHAS", False)
user_NOX = request.form.get("NOX", False)
user_RM = request.form.get("RM", False)
user_AGE = request.form.get("AGE", False)
user_DIS = request.form.get("DIS", False)
user_RAD = request.form.get("RAD", False)
user_TAX = request.form.get("TAX", False)
user_PTRATIO = request.form.get("PTRATIO", False)
user_B = request.form.get("B", False)
user_LSTAT = request.form.get("LSTAT", False)
# get user's phone nuber
user_phone_number = request.form.get("phone", False)
# instantiate the model
model = Modelling()
prediction = model.predictUserInput(user_CRIM, user_ZN, user_INDUS,
user_CHAS, user_NOX, user_RM, user_AGE,
user_DIS, user_RAD, user_TAX,
user_PTRATIO, user_B, user_LSTAT)
# append meaniningful message to the prediction
prediction = "Predicted Median value of owner-occupied homes in $1000's is: " + prediction
# if the phone number is valid, then send a message
snsService = Sns()
# user_phone_number = str(user_phone_number)
returnMessage = snsService.sendSMS(user_phone_number, prediction)
# check if user did not provide phone nubmer, then don't return any message from the SNS class
if len(user_phone_number) == 0:
returnMessage = ""
# prediction = 'RESULT'
return render_template('prediction.html',
result=[prediction, returnMessage])
def dt_model(self, X_train, y_train, X_val, y_val, cv_type='gridsearch'):
DecisionTreeClassifier_param = {
"criterion": ["gini", "entropy"],
"max_depth": [8],
"max_features": [0.7],
"min_samples_leaf": [10],
"min_samples_split": [3],
"random_state": [RANDOM_SEED]
}
model = Modelling().best_model_fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
clf=DecisionTreeClassifier(),
param=DecisionTreeClassifier_param,
cv_type=cv_type)
return model
def logit_model(self,
X_train,
y_train,
X_val,
y_val,
cv_type='gridsearch'):
LogisticRegression_param = {
"C": [0.25],
"max_iter": [135],
"penalty": ["l2"],
"random_state": [RANDOM_SEED]
}
model = Modelling().best_model_fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
clf=LogisticRegression(),
param=LogisticRegression_param,
cv_type=cv_type)
return model
def etc_model(self, X_train, y_train, X_val, y_val, cv_type='gridsearch'):
ExtraTreesClassifier_param = {
"n_estimators": [175],
"criterion": ['gini', 'entropy'],
"max_depth": [21],
"min_samples_split": [4],
"min_samples_leaf": [8],
"max_features": [0.5],
}
model = Modelling().best_model_fit(X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
clf=ExtraTreesClassifier(
random_state=RANDOM_SEED,
bootstrap=True,
n_jobs=-2,
warm_start=True),
param=ExtraTreesClassifier_param,
cv_type=cv_type)
return model
def main(data_directory_path, merge_csv_file_name, prepared_csv_file_name,
features_target_csv_file_name):
print("Model Process starts")
#path = "E:\PlusDental_Task\sample_data"
#merge_file_name = "data_merged.csv"
#prepared_file_name = "data_prepared.csv"
#feature_target_file_name = "features_target.csv"
start = time.time()
data_read_and_merge = DataReadAndMerge(data_directory_path,
merge_csv_file_name)
# data_read_and_merge.readAndMerge(path,merge_file_name)
data_prepare = DataPrepare(data_directory_path, merge_csv_file_name)
#data_prepare.dataPrepare(path, merge_file_name)
#data_prepared = pd.read_csv(os.path.join(data_directory_path, prepared_csv_file_name))
#print(data_prepared.head())
#print(data_prepared.shape)
#data_explore = DataExploration(data_prepared)
#data_explore.dataExploration(data_prepared)
feature_engineering = FeatureEngineering(data_directory_path,
prepared_csv_file_name)
#feature_engineering.featureEngineering(path,prepared_file_name)
modelling = Modelling(data_directory_path, features_target_csv_file_name)
#modelling.modelling(data_directory_path, features_target_csv_file_name)
model_pipeline = ModelPipeline(data_read_and_merge, data_prepare,
feature_engineering, modelling)
model_pipeline.fit(data_directory_path, merge_csv_file_name,
prepared_csv_file_name, features_target_csv_file_name)
print("Model Process ends", time.time() - start, "s")
def main(data_directory_path):
print("Model Process starts")
start = time.time()
data_read = DataRead(data_directory_path)
data_prepare = DataPrepare()
data_explore = DataExploration()
feature_engineering = FeatureEngineering()
modelling = Modelling()
model_pipeline = ModelPipeline(data_read, data_explore, data_prepare,
feature_engineering, modelling)
model_pipeline.fit(data_directory_path)
print("Model Process ends", time.time() - start, "s")
from modelling import Modelling import pandas as pd # Set file to process, read it in, and create a Model for it file_name = "docs/description.csv" description = pd.read_csv(file_name) model = Modelling(description)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import utils.helper_models as helper_models
from modelling import Modelling
from xgboost import plot_tree
#path = "E:\PlusDental_Task\presentation\Churn_Modelling.csv"
data_directory_path = "E:\PlusDental_Task\presentation"
features_target_csv_file_name = "Churn_Modelling.csv"
#data_raw = pd.read_csv(path)
#print(data_raw.shape)
# Checking missing values Just for confirmation
# data_check = helper_models.missing_values_table(data_raw)
# print('Missing values in a column with the percentage', data_check)
modelling = Modelling(data_directory_path, features_target_csv_file_name)
modelling.modelling(data_directory_path, features_target_csv_file_name)
def setUp(self):
self.description = pd.read_csv("docs/description.csv")
self.description_1000 = pd.read_csv("docs/description_1000.csv")
self.mdl = Modelling(self.description)
