def to_df(csv: str) -> pd.DataFrame:
data = pd.read_csv(csv)
loader = DataLoader()
loader.fit(data)
return loader.load_data()
#prediction1 = Predictor().predict(X_test)
#loaded_model = pickle.load(open('models/KNN.pickle', 'rb'))
#print(loaded_model.score(test_set[x_columns].values, test_set[y_column].values))
#print(accuracy_score(y_test,prediction))
#print(accuracy_score(y,prediction1))
#print(X_test)
PREDICT_ROUTE = "http://127.0.0.1:8000/predict"
info = specifications['description']
x_columns, y_column, metrics = info['X'], info['y'], info['metrics']
train_set = pd.read_csv(TRAIN_CSV, header=0)
test_set = pd.read_csv(VAL_CSV, header=0)
train_x, train_y = train_set[x_columns], train_set[y_column]
test_x, test_y = test_set[x_columns], test_set[y_column]
loader = DataLoader()
loader.fit(train_x)
train_processed = loader.load_data()
loader = DataLoader()
loader.fit(test_x)
test_processed = loader.load_data()
trained = Estimator.fit(train_processed, train_y)
trained_predict = Estimator.predict(trained, test_processed)
trained_score = round(eval(metrics)(test_y, trained_predict), 2)
req_data = {'data': json.dumps(test_x.to_dict())}
response = requests.get(PREDICT_ROUTE, data=req_data)
api_predict = response.json()['prediction']
api_score = round(eval(metrics)(test_y, api_predict), 2)
print(trained_score)
print(api_score)
assert trained_score == api_score
from sklearn.linear_model import LogisticRegression
from utils.dataloader import DataLoader
from settings.constants import TRAIN_CSV
with open('settings/specifications.json') as f:
specifications = json.load(f)
raw_train = pd.read_csv(TRAIN_CSV)
x_columns = specifications['description']['X']
y_column = specifications['description']['y']
x_raw = raw_train[x_columns]
loader = DataLoader()
loader.fit(x_raw)
X = loader.load_data()
y = raw_train.Response
model = LogisticRegression(C=0.01, penalty='l1', solver='liblinear')
model.fit(X, y)
with open('models/log_reg.pickle', 'wb')as f:
pickle.dump(model, f)
import pickle
import json