def preprocessing_walmart(dataset):# 2nd function definition processing
dataset['Day'] = pd.to_datetime(dataset['Date'])
#using pandas pipeline
panda_pipe = pdp.ApplyByCols('Day',lambda x: (x.day//7)+1,'Week_no',drop = False)
#converting given day into week of the month
panda_pipe += pdp.ApplyByCols('Day',lambda x: x.month,'month',drop = False)
#getting month from the date
panda_pipe += pdp.ColDrop(['Date','Day'])
dataset = panda_pipe(dataset)
dataset['Lag2'] = dataset['Weekly_Sales'].shift(2)
dataset['Lag3'] = dataset['Weekly_Sales'].shift(3)
dataset['Lag4'] = dataset['Weekly_Sales'].shift(4)
dataset['Lag5'] = dataset['Weekly_Sales'].shift(5)
dataset['Lag6'] = dataset['Weekly_Sales'].shift(6)
to_be_predicted = dataset['Weekly_Sales']
dataset = dataset.drop(columns = ['Weekly_Sales'])
X_train,X_test,Y_train,Y_test = train_test_split(dataset, to_be_predicted, random_state = 42, test_size = 0.3)
return (X_train,Y_train,X_test)
def main():
print("Qual modelo quer treinar?")
value = input(
"1: Regressão Logistica, 2: Multinomial Naive Bayes, 3: CNN, digite o numero correspondente ao modelo:"
)
# import dataset
train, test = common_modules.merge_files()
if value == '1':
pipeline = pdp.ApplyByCols("texto",
common_modules.tag_remove,
"clean_texto",
drop=False)
pipeline += pdp.ApplyByCols("clean_texto", common_modules.trat_texto)
train = pipeline(train)
test = pipeline(test)
print("Treinando modelo de regressão logistica")
regressao_logistica.train_model(train.texto, train.label, test.texto,
test.label)
if value == '2':
pipeline = pdp.ApplyByCols("texto",
common_modules.tag_remove,
"clean_texto",
drop=False)
pipeline += pdp.ApplyByCols("clean_texto", common_modules.trat_texto)
train = pipeline(train)
test = pipeline(test)
print("treinando modelo de Multinomial Naive Bayes")
naive_bayes.train_model(train.texto, train.label, test.texto,
test.label)
if value == '3':
import CNN
print("treinando modelo de CNN")
CNN.train_model(train, test)
def make_conversion_pipe():
"""
Creates the Pandas Pipeline for the transformation of the UN Geoscheme DataFrame
"""
pipeline = pdp.ColRename({i: str(i) for i in range(0, 5)})
pipeline += pdp.ApplyByCols(
['country/region', 'numeric', '0', '1', '2', '3', '4'],
func=replace_new_lines)
pipeline += pdp.ApplyByCols(['numeric', '0', '1', '2', '3', '4'],
func=clean_out_world)
pipeline += pdp.DropNa(axis=1, how='all')
return pipeline
def main():
train, test = common_modules.merge_files()
pipeline = pdp.ApplyByCols("texto",
common_modules.tag_remove,
"clean_texto",
drop=False)
pipeline += pdp.ApplyByCols("clean_texto", common_modules.trat_texto)
train = pipeline(train)
test = pipeline(test)
reg_log(train.texto, train.label, test.texto, test.label)
naive_bayes(train.texto, train.label, test.texto, test.label)
def _original_code():
start = time.time()
salesdata = pd.read_csv("processed_salesdata.csv")
pline = pdp.PdPipeline([
pdp.Schematize(COLUMNS),
pdp.ApplyByCols("category_group", lambda x: "tops"
if x == "tops" else "other"),
pdp.ApplyByCols(["date", "shelf_date", "end_of_season"],
pd.to_datetime),
pdp.ApplyToRows(lambda row: pd.Series({
"standard_amount":
row["original_price"] * row["sales"],
"sales_discount":
0 if (row["original_price"] * row["sales"] <= 0) else row[
"sales_amount"] / ((row["original_price"] * row["sales"])),
"week":
int(row["date"].strftime('%W')),
"days_on_counter":
(row["date"] - row["shelf_date"]) / np.timedelta64(1, 'D'),
"life_cycle": (row["end_of_season"] - row["shelf_date"]) /
(np.timedelta64(1, 'D')),
"C1":
1 if row["category_group"] == "tops" else 0,
"C2":
1 if row["category_group"] == "other" else 0,
"sales":
0 if row["sales"] < 0 else row["sales"],
"passenger_flow":
0 if row["passenger_flow"] < 0 else (row["passenger_flow"]),
"plus_purchase":
0 if row["plus_purchase"] < 0 else (row["plus_purchase"]),
})),
pdp.AdHocStage(
lambda df: df[df["days_on_counter"] <= df["life_cycle"]]),
pdp.ColDrop("activity_level")
])
salesdata = pline.apply(salesdata, verbose=True, exraise=True)
salesdata_cumitems = salesdata[[
"SKC", "date", "sales", "passenger_flow", "plus_purchase"
]].sort_values(by=["SKC", "date"]).groupby(['SKC']).cumsum()
salesdata_cumitems.columns = [
"total_sales", "total_passenger_flow", "total_plus_purchase"
]
salesdata["total_sales"] = salesdata_cumitems["total_sales"]
salesdata["total_passenger_flow"] = salesdata_cumitems[
"total_passenger_flow"]
salesdata["total_plus_purchase"] = salesdata_cumitems[
"total_plus_purchase"]
print("consumed time(s)=", time.time() - start)
def uniformize_boolean(columns):
start_time = time.time()
true_strings = ['t', 'true', 'yes', 'y', True]
false_strings = ['f', 'false', 'n', 'no', False]
func_true = lambda x: 1.0 if x in true_strings else x
func_false = lambda x: 0.0 if x in false_strings else x
result = pdp.ApplyByCols(columns, func_true) + pdp.ApplyByCols(
columns, func_false)
time_elapsed = time.time() - start_time
print("uniformize_boolean:", time_elapsed)
return result
def check_sk_pipeline():
pline = pdp.make_pdpipeline(
pdp.ApplyByCols("ph", lambda x: x - 1),
# pdp.Bin({"ph": [0, 3, 5, 12]}),
pdp.Encode(["type", "lbl"]),
)
print(pline)
model_pline = make_pipeline(
pdp.FreqDrop(2, "lbl"),
LogisticRegression(),
)
print(model_pline)
train = _train_df()
res_train = pline(train)
print("Processed train set: {}".format(res_train))
x_train, y_train = x_y_by_col_lbl(res_train, "lbl")
model_pline = model_pline.fit(x_train, y_train)
print("Fitted model pipeline: {}".format(model_pline))
test = _test_df()
res_test = pline(test)
print("Processed test set: {}".format(res_test))
x_test, y_test = x_y_by_col_lbl(res_test, "lbl")
predictions = model_pline.predict(x_test)
print("predictions: {}".format(predictions))
def uniformize_missing(columns):
start_time = time.time()
missing_values_strings = ['NaN', '??', '*', 'UNK', '-', '###']
func = lambda x: np.nan if x in missing_values_strings else x
time_elapsed = time.time() - start_time
result = pdp.ApplyByCols(columns, func)
print("uniformize_missing:", time_elapsed)
return result
def uniformize_percentage():
start_time = time.time()
percentage_columns = ['host_response_rate']
func = lambda x: float(x[:-1]) if type(x) == str else x
result = pdp.ApplyByCols(percentage_columns, func)
time_elapsed = time.time() - start_time
print("uniformize_percentage:", time_elapsed)
return result
def main():
train, test = common_modules.merge_files()
pipeline = pdp.ApplyByCols("texto",
common_modules.tag_remove,
"clean_texto",
drop=False)
pipeline += pdp.ApplyByCols("clean_texto", common_modules.trat_texto)
train = pipeline(train)
print("Fazendo gridSearch da Regressão logistica")
score_log_reg, param_log_reg = reg_log(train.texto, train.label)
print(
f"Regressão linear best score {score_log_reg}, com parâmetros {param_log_reg} "
)
print("Fazendo gridSearch da SVM")
score_svm, param_svm = sup_vec(train.texto, train.label)
print(f"suport vector best score {score_svm}, com parâmetros {param_svm} ")
print("Fazendo gridSearch da Naive bayes")
score_nb, param_nb = naive_bayes(train.texto, train.label)
print(
f"Multinomial Naive bayes best score {score_nb}, com parâmetros {param_nb} "
)
def uniformize_monetary():
start_time = time.time()
monetary_columns = [
'price', 'weekly_price', 'monthly_price', 'security_deposit',
'cleaning_fee', 'extra_people'
]
func = lambda x: float(x[1:].replace(',', '')) if type(x) == str else x
result = pdp.ApplyByCols(monetary_columns, func)
time_elapsed = time.time() - start_time
print("uniformize_monetary:", time_elapsed)
return result
cnxn, cursor = sql_data.connect()
df = sql_data.executeQueryFromFile(cnxn)
sql_subwaystations = bsql.SQL(sql_file_name_coordinates_subwaystations,
sql_file_path)
cnxn, cursor = sql_subwaystations.connect()
df_subwaystations = sql_subwaystations.executeQueryFromFile(cnxn)
sql_addresses = bsql.SQL(sql_file_name_coordinates_addresses, sql_file_path)
cnxn, cursor = sql_addresses.connect()
df_addresses = sql_addresses.executeQueryFromFile(cnxn)
geodesic(df_subwaystations[['Latitude', 'Longitude']],
df_addresses[['Latitude', 'Longitude']])
pipeline = pdp.ApplyByCols('District', District_transformation, 'District')
pipeline += pdp.ApplyByCols('BuiltYear', builtYear_transformation, 'BuiltYear')
pipeline += pdp.RowDrop({'District': lambda x: x == None})
pipeline += pdp.RowDrop({'OperatingCostInSek': lambda x: pd.isnull(x) == True})
pipeline += pdp.RowDrop({'NumberOfRooms': lambda x: x == 0})
pipeline += pdp.RowDrop({'FloorNumber': lambda x: pd.isnull(x) == True})
pipeline += pdp.RowDrop({'BuiltYear': lambda x: pd.isnull(x) == True})
pipeline += pdp.OneHotEncode('District')
pipeline += pdp.OneHotEncode('BuiltYear')
pipeline += pdp.ColDrop(['Address'])
df_pipeline = pipeline(df)
variables = GoMining(df_pipeline)
MiningReport(variables)
formula = 'SoldPricePerSquaredMeterInSek ~ MonthlyChargeInSek + \
data = pd.read_csv('C:\\Users\\13810\\tmdb_5000_movies.csv')
# In[8]:
import pdpipe as pdp#导入pdpipe包
# In[13]:
#创建pdp.pdppipeline ,其中pdp.ColDrop和pdp.ApplyByCols和pdp.Rowdrop都是pdpipe中常用的API，是固定写法，有固定的语法格式
first_pipeline = pdp.PdPipeline(
[pdp.ColDrop("original_title"),#ColDrop用于对指定单个或多个列进行丢弃
pdp.ApplyByCols(columns=['title'], func=lambda x: x.lower()),#对每一列进行应用
pdp.RowDrop({'vote_average': lambda x: x <= 7, 'original_language': lambda x: x != 'en'}),#对行进行删除
pdp.ApplyByCols(columns=['genres'], func=lambda x: [item['name'] for item in eval(x)].__len__(), result_columns=['genres_num']),
pdp.RowDrop({'genres_num': lambda x: x <= 5})]
)
# In[19]:
data1=first_pipeline(data, verbose=True).reset_index(drop=True)
pdp.RowDrop({'vote_average': lambda x: x <= 7, 'original_language': lambda x: x != 'en'}),
pdp.ApplyByCols(columns=['genres'], func=lambda x: [item['name'] for item in eval(x)].__len__(), re
# In[20]:
!gsutil -m cp -r gs://{bucket_name}/* /content/drive/My\ Drive/CoronaTweets/
base_db_folder = '/content/drive/My Drive/CoronaTweets'
tweet_db_paths = [
# incomplete data - '/corona_tweets_1M.db/corona_tweets_1M.db', # 27.02.2020 10:36 01.03.2020 18:24 1578957
# malformed - '/corona_tweets_2M_2/corona_tweets_2M_2.db', # 02.03.2020 17:27 07.03.2020 4:57 2268665
'/corona_tweets_3M/tweets.db', # 07.03.2020 5:06 14.03.2020 4:46 7472368
'/corona_tweets_1M/tweets.db', # 14.03.2020 5:23 15.03.2020 3:16 1903768
'/corona_tweets_2M_3/tweets.db', # 15.03.2020 3:28 16.03.2020 4:31 2081576
'/corona_tweets_1M_2/tweets.db', # 16.03.2020 4:38 17.03.2020 3:08 1889781
'/corona_tweets_2L/tweets.db' # 17.03.2020 3:12 17.03.2020 6:10 280304
]
pipeline = pdp.PdPipeline([
pdp.ColRename({'unix': 'tweet_date'}),
pdp.ApplyByCols('sentiment', is_positive, 'is_positive', drop=False),
pdp.ApplyByCols('sentiment', is_negative, 'is_negative', drop=False),
pdp.ApplyByCols('sentiment', is_neutral, 'is_neutral', drop=False),
])
tweets_df = pd.DataFrame()
for tweets_db in tweet_db_paths:
full_tweet_db_path = base_db_folder + tweets_db
print(dt.datetime.now(), "Processing started: ", full_tweet_db_path)
conn = sqlite3.connect(full_tweet_db_path)
c = conn.cursor()
df_pie = pd.read_sql("SELECT * FROM sentiment", conn)
df_pie['unix'] = pd.to_datetime(df_pie['unix'], unit='ms').dt.date # cast to date
df = pipeline.apply(df_pie).sort_values(by=['tweet_date'])