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values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
101 | df_out['ConfirmedCases'] = (1/2)*(df_out['ConfirmedCases'] + df_out2['ConfirmedCases'])
df_out['Fatalities'] = (1/2)*(df_out['Fatalities'] + df_out2['Fatalities']) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
102 | df_out['ConfirmedCases'] = df_out['ConfirmedCases'].round().astype(int)
df_out['Fatalities'] = df_out['Fatalities'].round().astype(int) | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
103 | df_out.tail() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
104 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 9 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 12"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
105 | train_df = pd.read_csv("../input/covid19-global-forecasting-week-2/train.csv")
test_df = pd.read_csv("../input/covid19-global-forecasting-week-2/test.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 8,660,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 11 | A |
106 | train_df.head(50) | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
107 | '''
from pandas_profiling import ProfileReport
train_profile = ProfileReport(train_df, title='Pandas Profiling Report', html={'style':{'full_width':True}})
train_profile
''' | No | 3 | 22 | Environment | import_modules | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
108 | train_df.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
109 | common_value = "UNKNOWN"
# Replacing all the Province_State that are null by the Country_Region values
train_df.Province_State.fillna(train_df.Country_Region, inplace=True)
test_df.Province_State.fillna(test_df.Country_Region, inplace=True)
# Handling the Date column
# 1. Converting the object type column into dateti... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
110 | #Dropping the date column
train_df.drop("Date", inplace = True, axis = 1)
test_df.drop("Date", inplace = True, axis = 1) | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
111 | train_df.Province_State.value_counts() | No | 5 | 72 | EDA | count_values | 8,660,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 11 | B |
112 |
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
train_df.Country_Region = le.fit_transform(train_df.Country_Region)
train_df['Province_State'] = le.fit_transform(train_df['Province_State'])
test_df.Country_Region = le.fit_transform(test_df.Country_Region)
test_df['Province_State'] = le.fit_transfo... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 14 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
114 | \ | No | 3 | 20 | Data_Transform | categorify | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
115 | '''
# Removing duplicate entries
train_df = train_df.loc[:,~train_df.columns.duplicated()]
test_df = test_df.loc[:,~test_df.columns.duplicated()]
print (test_df.shape)
''' | No | 4 | 19 | Data_Transform | remove_duplicates | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
116 | '''
# Dropping the object type columns
train_df.drop(objList, axis=1, inplace=True)
test_df.drop(objList, axis=1, inplace=True)
print (train_df.shape)
''' | No | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
117 | \ | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
118 | test_df.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
119 | X_train = train_df.drop(["Id", "ConfirmedCases", "Fatalities"], axis = 1)
Y_train_CC = train_df["ConfirmedCases"]
Y_train_Fat = train_df["Fatalities"]
#X_test = test_df.drop(["ForecastId"], axis = 1)
X_test = test_df.drop(["ForecastId"], axis = 1) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 8,660,923 | 1 | A | 1 | 0 | 0 | 0 | 5 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 11 | B |
121 | \ | No | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
122 | from sklearn.model_selection import ShuffleSplit, cross_val_score
skfold = ShuffleSplit(random_state=7) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
123 |
#1.Ridge Regression
#Model import
from sklearn.linear_model import Ridge
#train classifier
reg_CC = Ridge(alpha=1.0)
reg_Fat = Ridge(alpha=1.0)
#Cross Validation to calculate the score
score_CC = cross_val_score(reg_CC, X_train, Y_train_CC, cv = skfold)
score_Fat = cross_val_score(reg_Fat, X_train, Y_train_Fat, c... | No | 5 | 84 | Other | not_enough_vertices | 8,660,923 | 1 | A | 1 | 0 | 0 | 0 | 5 | 6 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 11 | A |
124 |
#2.Lasso Regression
#Model import
from sklearn import linear_model
#train classifier
reg_CC = linear_model.Lasso(alpha=0.1)
reg_Fat = linear_model.Lasso(alpha=0.1)
#Cross Validation to calculate the score
score_CC = cross_val_score(reg_CC, X_train, Y_train_CC, cv = skfold)
score_Fat = cross_val_score(reg_Fat, X_t... | No | 5 | 28 | Model_Train | compute_train_metric | 8,660,923 | 1 | A | 1 | 0 | 0 | 0 | 5 | 6 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 11 | A |
125 |
#3. SVM
#Model import
from sklearn import svm
#train classifier
reg_CC = svm.SVC()
reg_Fat = svm.SVC()
#Cross Validation to calculate the score
score_CC = cross_val_score(reg_CC, X_train, Y_train_CC, cv = skfold)
score_Fat = cross_val_score(reg_Fat, X_train, Y_train_Fat, cv = skfold)
#Print the scores
print (sco... | No | 5 | 28 | Model_Train | compute_train_metric | 8,660,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 11 | A |
126 |
#3. ElasticNet
#Model import
from sklearn.linear_model import ElasticNet
#train classifier
reg_CC = ElasticNet(random_state=0)
reg_Fat = ElasticNet(random_state=0)
#Cross Validation to calculate the score
score_CC = cross_val_score(reg_CC, X_train, Y_train_CC, cv = skfold)
score_Fat = cross_val_score(reg_Fat, X_tr... | No | 5 | 28 | Model_Train | compute_train_metric | 8,660,923 | 2 | B | 2 | 0 | 0 | 0 | 10 | 6 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 11 | B |
127 |
#5. LinearRegression
#Model import
from sklearn.linear_model import LinearRegression
#train classifier
reg_CC = LinearRegression()
reg_Fat = LinearRegression()
#Cross Validation to calculate the score
score_CC = cross_val_score(reg_CC, X_train, Y_train_CC, cv = skfold)
score_Fat = cross_val_score(reg_Fat, X_train... | No | 5 | 28 | Model_Train | compute_train_metric | 8,660,923 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 11 | A |
128 | \ | No | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
129 | \ | No | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
130 | \ | No | 3 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
131 | \ | No | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
132 |
#5. BaggingClassifier
from sklearn.ensemble import BaggingRegressor
from sklearn.tree import DecisionTreeRegressor
clf_bgr_CC = BaggingRegressor(base_estimator = DecisionTreeRegressor())
clf_bgr_Fat = BaggingRegressor(base_estimator = DecisionTreeRegressor())
rmsle_bgr_CC = test_model(clf_bgr_CC, "CC")
rmsle_bgr_Fat... | No | 5 | 84 | Other | not_enough_vertices | 8,660,923 | 3 | C | 6 | 0 | 0 | 0 | 30 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | C |
133 | \ | No | 4 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
135 | reg_CC.fit(X_train, Y_train_CC)
Y_pred_CC = reg_CC.predict(X_test)
reg_Fat.fit(X_train, Y_train_Fat)
Y_pred_Fat = reg_Fat.predict(X_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
136 | print (Y_pred_Fat) | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
137 | #Using pd.to_datetime for adding new features
df_train['Date'] = pd.to_datetime(df_train['Date'])
df_train.insert(1,'Week',df_train['Date'].dt.week)
df_train.insert(2,'Day',df_train['Date'].dt.day)
df_train.insert(3,'DayofWeek',df_train['Date'].dt.dayofweek)
df_train.insert(4,'DayofYear',df_train['Date'].dt.dayofyear)
... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
138 | # Replacing all the Province_State that are null by the Country_Region values
df_train.Province_State.fillna(df_train.Country_Region, inplace=True)
df_test.Province_State.fillna(df_test.Country_Region, inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
139 | from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df_train.Country_Region = le.fit_transform(df_train.Country_Region)
df_train['Province_State'] = le.fit_transform(df_train['Province_State'])
df_test.Country_Region = le.fit_transform(df_test.Country_Region)
df_test['Province_State'] = le.fit_transfor... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 14 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
140 | #One Hot Encoding columns
def one_hot(df, cols):
"""
@param df pandas DataFrame
@param cols a list of columns to encode
@return a DataFrame with one-hot encoding
"""
i = 0
for each in cols:
#print (each)
dummies = pd.get_dummies(df[each], prefix=each, drop_first= True)
... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 3 | 3 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
141 | #Handling categorical data
objList = df_train.select_dtypes(include = "object").columns
df_train = one_hot(df_train, objList)
df_test = one_hot(df_test, objList)
print (df_train.shape) | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
142 | #Avoiding duplicated data.
df_train = df_train.loc[:,~df_train.columns.duplicated()]
df_test = df_test.loc[:,~df_test.columns.duplicated()]
print (df_test.shape) | No | 4 | 19 | Data_Transform | remove_duplicates | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
143 | #reading data
data = pd.read_csv('../input/covid19-global-forecasting-week-2/train.csv')
test_data = pd.read_csv('../input/covid19-global-forecasting-week-2/test.csv')
submission = pd.read_csv('../input/covid19-global-forecasting-week-2/submission.csv')
print(data.shape)
print(test_data.shape)
print(submission.shape) | No | 4 | 45 | Data_Extraction | load_from_csv | 8,592,598 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/covid19-global-forecasting-week-2/train.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}, {"detector": "general_ml", "frame... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
144 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | No | 4 | 22 | Environment | import_modules | 8,667,455 | 1 | A | 0 | 2 | 3 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 21"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
145 | dftrain = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/train.csv")
dftrain["GrowthRate"] = dftrain["ConfirmedCases"] / dftrain.ConfirmedCases.shift(1)
print(dftrain.Date.unique())
dftest = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/test.csv")
print(dftest.columns.values)
dfsubmission ... | No | 3 | 45 | Data_Extraction | load_from_csv | 8,667,455 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 7 | 5 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 3 | B |
146 | growth_per_country = {}
means_per_country = {}
last_per_country = {}
def train(country, region, growth):
if growth < 2.0:
print(growth)
growth_list = growth_per_country.get((country,region), list())
growth_list.append(growth)
growth_per_country[(country,region)] = growth_list
... | No | 2 | 7 | Model_Train | train_model | 8,667,455 | 1 | A | 2 | 4 | 2 | 0 | 7 | 14 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 17"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Che... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "Missing function docstring"] | 3 | C |
147 | ## Training
for row in dftrain.itertuples():
train(row.Country_Region, row.Province_State, row.GrowthRate) | No | 3 | 7 | Model_Train | train_model | 8,667,455 | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 3 | B |
148 | ## Current submission
for row in dftest.itertuples():
if(row.ForecastId%100 == 0):
print(row.ForecastId)
if type(row.Province_State)!=str:
dfnow = dftrain[dftrain.Country_Region == row.Country_Region]
else:
dfnow = dftrain[dftrain.Country_Region == row.Country_Region][dftrain.Provinc... | No | 3 | 27 | Model_Train | predict_on_train | null | 1 | A | 1 | 4 | 9 | 1 | 2 | 19 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
149 | print(dfsubmission.head(60)) | No | 5 | 41 | Debug | show_table | 8,667,455 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
150 | dfsubmission["Date"] = dftest["Date"]
dfsub = dfsubmission[dftest.Country_Region == 'Ireland'][dftest.Province_State.isnull()]
dfsub[:20].plot("Date", "ConfirmedCases")' | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
151 | df = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/train.csv")
df.head(40)
print(df.columns.values)
df["GrowthRate"] = df["ConfirmedCases"] / df.ConfirmedCases.shift(1)
df["Growth"] = df["ConfirmedCases"] - df.ConfirmedCases.shift(1)
df["PredictedCasesByRate"] = df["ConfirmedCases"].shift(1) * df.Growth... | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
152 | dftest = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/test.csv")
dftest.head(40) | No | 4 | 45 | Data_Extraction | load_from_csv | 8,667,455 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/covid19-global-forecasting-week-2/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
153 | dfg = df.groupby([df.Country_Region, df.Province_State])
dfg.head() | No | 3 | 60 | Data_Transform | groupby | 8,667,455 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
154 | import numpy as np
import pandas as pd
df = pd.read_csv("../input/covid19-global-forecasting-week-2/train.csv")
df.shape | No | 3 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/covid19-global-forecasting-week-2/train.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
155 | loc_group = ["Province_State", "Country_Region"]
def preprocess(df):
df["Date"] = df["Date"].astype("datetime64[ms]")
for col in loc_group:
df[col].fillna("none", inplace=True)
return df
df = preprocess(df)
sub_df = preprocess(pd.read_csv("../input/covid19-global-forecasting-week-2/test.csv"))
df... | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 2 | 1 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
156 | # Dropping the object type columns
df_train.drop(objList, axis=1, inplace=True)
df_test.drop(objList, axis=1, inplace=True)
print (df_train.shape) | No | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
157 | df["Date"].min(), df["Date"].max() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
158 | TARGETS = ["ConfirmedCases", "Fatalities"]
for col in TARGETS:
df[col] = np.log1p(df[col]) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
159 | for col in TARGETS:
df["prev_{}".format(col)] = df.groupby(loc_group)[col].shift() | No | 5 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
160 | df = df[df["Date"] > df["Date"].min()].copy()
df.head() | No | 4 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
161 | df_train | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
162 | X = df_train.drop(['Date', 'ConfirmedCases', 'Fatalities'], axis=1)
y = df_train[['ConfirmedCases', 'Fatalities']] | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
163 | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import BayesianRidge
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_... | No | 5 | 22 | Environment | import_modules | 8,609,050 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 13 | A |
164 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
165 | y_train.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
166 | n_folds = 5
cv = KFold(n_splits = 5, shuffle=True, random_state=42).get_n_splits(X_train.values) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
167 | def predict_scores(reg_alg):
r2 = make_scorer(r2_score)
m = reg_alg()
m.fit(X_train, y_train['ConfirmedCases'])
y_pred = m.predict(X_test)
m_r = cross_val_score(m, X_train, y_train['ConfirmedCases'], cv=cv, scoring = r2)
sc_Cases.append(m_r)
m.fit(X_train, y_train['Fatalities'])
y_p... | No | 5 | 3 | Hyperparam_Tuning | find_best_model_class | 8,609,050 | 1 | A | 1 | 2 | 1 | 0 | 5 | 17 | ok | 14 | 7 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Missing function docstring", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | B |
168 | sc_Cases | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
169 | sc_Fatalities | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
170 | from sklearn.ensemble import BaggingRegressor | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
171 |
#Hyperparameter tuning
from sklearn.model_selection import RandomizedSearchCV,GridSearchCV
param_grid = {
'n_estimators':[10, 30, 50, 100,250,500,750,1000,1250,1500,1750],
'max_samples':[2,4,6,8,10,20,40,60,100],
"max_features": [0.5, 1.0],
'n_jobs':[-2, -1, 1,... | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
172 | model1 = clf_CC
model1.fit(X_train, y_train['ConfirmedCases'])
model2 = clf_Fat
model2.fit(X_train, y_train['Fatalities']) | No | 5 | 7 | Model_Train | train_model | 8,609,050 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 10 | 8 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | B |
173 | df_test['ConfirmedCases'] = model1.predict(df_test.drop(['Date', 'ForecastId'], axis=1))
df_test['Fatalities'] = model2.predict(df_test.drop(['Date', 'ForecastId', 'ConfirmedCases'], axis=1)) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
174 | import warnings
warnings.filterwarnings('ignore')
df_results = df_test[['ForecastId', 'ConfirmedCases', 'Fatalities']]
df_results['ConfirmedCases'] = df_results['ConfirmedCases'].astype(int)
df_results['Fatalities'] = df_results['Fatalities'].astype(int)
df_results.head() | No | 4 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
175 | df_results.to_csv('submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
176 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 11 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 13"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
177 | import plotly.graph_objects as go
import matplotlib.pyplot as plt
from tqdm import tqdm
import time
from datetime import datetime
from pathlib import Path
from sklearn import preprocessing
import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, GRU
from keras.callbac... | No | 5 | 22 | Environment | import_modules | 8,678,201 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
178 | train = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/train.csv")
test = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/test.csv")
train.tail() | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
179 | test.tail() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
180 | train.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
182 | train['Date'] = pd.to_datetime(train['Date'])
test['Date'] = pd.to_datetime(test['Date'])
train['Country_Region'] = train['Country_Region'].astype(str)
# train['Province_State'] = train['Province_State'].astype(str)
test['Country_Region'] = test['Country_Region'].astype(str)
# test['Province_State'] = test['Province_St... | No | 5 | 16 | Data_Transform | data_type_conversions | null | 2 | B | 2 | 0 | 0 | 0 | 10 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
183 | EMPTY_VAL = "EMPTY_VAL"
def fillState(state, country):
if state == EMPTY_VAL: return country
return state
train['Province_State'].fillna(EMPTY_VAL, inplace=True)
train['Province_State'] = train.loc[:, ['Province_State', 'Country_Region']].apply(lambda x : fillState(x['Province_State'], x['Country_Region']), ... | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
184 | le = preprocessing.LabelEncoder()
train['country_encoder'] = le.fit_transform(train['Country_Region'])
train['date_int'] = train['Date'].apply(lambda x: datetime.strftime(x, '%m%d')).astype(int)
test['country_encoder'] = le.transform(test['Country_Region'])
test['date_int'] = test['Date'].apply(lambda x: datetime.strf... | No | 4 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
185 | le = preprocessing.LabelEncoder()
train['province_encoder'] = le.fit_transform(train['Province_State'])
test['province_encoder'] = le.transform(test['Province_State']) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
186 | #TODO: takes 44m ish, consider multi-processing, multi-cores, run in GPU
#TODO: create data_generate func
from joblib import parallel_backend
start_time = time.time()
country = train['Country_Region'].drop_duplicates()
train_df = train.copy()
train_df.rename(columns={'Date': 'date', 'ConfirmedCases': 'cc_cases', 'Fata... | No | 4 | 8 | Data_Transform | feature_engineering | 8,678,201 | 1 | A | 4 | 4 | 20 | 0 | 10 | 25 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 12"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "InPlace Checker"] | 4 | B |
187 | # train_df.to_csv(Path('/kaggle/working', 'train_df.csv'))
# saved locally, reload it
train_df = pd.read_csv(Path('/kaggle/working/', 'train_df.csv'), index_col = 0, parse_dates = ['date'])
train_df[train_df['country'] == 'Italy'].tail() | No | 4 | 45 | Data_Extraction | load_from_csv | 8,678,201 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Hardcoded file path"] | 4 | B |
188 | #TODO: walk forward validation
def split_train_val(df, val_ratio):
val_len = int(len(df) * val_ratio)
train_set = df[:-val_len]
val_set = df[-val_len:]
return train_set, val_set | No | 5 | 13 | Data_Transform | split | null | 1 | A | 1 | 1 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
189 | test_fixed_cols = ['ForecastId', 'Province_State', 'Country_Region', 'Date']
fixed_cols = ['Id', 'province', 'country', 'date']
output_cols = ['cc_cases', 'ft_cases']
input_cols = list(set(train_df.columns.to_list()) - set(fixed_cols) - set(output_cols))
print('output columns are ', output_cols)
print('input columns ar... | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
190 | # split to cumulative and fatal features and build 2 separate models
# split to train and validation set
cc_input = ['cc_cases_1', 'cc_cases_2', 'cc_cases_3', 'cc_cases_4', 'cc_cases_5', 'cc_cases_6', 'cc_cases_7', 'country_encoder', 'province_encoder', 'date_int']
ft_input = ['ft_cases_1', 'ft_cases_2', 'ft_cases_3', ... | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
191 | idx = np.random.RandomState(seed=42).permutation(train_X_cc.index)
train_X_cc = train_X_cc.reindex(idx)
train_y_cc = train_y_cc.reindex(idx)
train_X_ft = train_X_ft.reindex(idx)
train_y_ft = train_y_ft.reindex(idx)
# train_y_cc.tail() | No | 5 | 15 | Data_Transform | randomize_order | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
192 | # normalization
X_scaler_cc = MinMaxScaler()
X_train_cc = X_scaler_cc.fit_transform(train_X_cc)
X_val_cc = X_scaler_cc.transform(val_X_cc) # intput/output 2D array-like
y_scaler_cc = MinMaxScaler()
y_train_cc = y_scaler_cc.fit_transform(train_y_cc)
y_val_cc = y_scaler_cc.transform(val_y_cc) # array-like | No | 5 | 18 | Data_Transform | normalization | 8,678,201 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 12 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | D |
193 | X_scaler_ft = MinMaxScaler()
X_train_ft = X_scaler_ft.fit_transform(train_X_ft)
X_val_ft = X_scaler_ft.transform(val_X_ft) # intput/output 2D array-like
y_scaler_ft = MinMaxScaler()
y_train_ft = y_scaler_ft.fit_transform(train_y_ft)
y_val_ft = y_scaler_ft.transform(val_y_ft) # array-like | No | 5 | 18 | Data_Transform | normalization | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
194 | print('Validate if train and test is splited correctly for 2 cases: ')
print('cumulative cases training has shape ', X_train_cc.shape, y_train_cc.shape)
print('fatal cases training has shape ', X_train_ft.shape, y_train_ft.shape)
print('cumulative cases valid has shape ', X_val_cc.shape, y_val_cc.shape)
print('fatal ca... | No | 3 | 41 | Debug | show_table | null | 1 | A | 1 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
195 | # if choose to not apply normalization, however it generates NaN in output...
X_train_cc = train_X_cc.to_numpy()
X_val_cc = val_X_cc.to_numpy()
X_train_ft = train_X_ft.to_numpy()
X_val_ft = val_X_ft.to_numpy()
y_train_cc = train_y_cc.to_numpy()
y_val_cc = val_y_cc.to_numpy()
y_train_ft = train_y_ft.to_numpy()
y_val_... | No | 3 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
196 | # for LSTM, intput.shape = (n_samples, 1, n_features)
X_train_cc = X_train_cc.reshape(X_train_cc.shape[0], 1, X_train_cc.shape[1])
X_val_cc = X_val_cc.reshape(X_val_cc.shape[0], 1, X_val_cc.shape[1])
X_train_ft = X_train_ft.reshape(X_train_ft.shape[0], 1, X_train_ft.shape[1])
X_val_ft = X_val_ft.reshape(X_val_ft.shape... | No | 3 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
197 | import pandas as pd
import datetime
import lightgbm as lgb
import numpy as np
from sklearn import preprocessing | No | 4 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
198 | train = pd.read_csv("../input/covid19-global-forecasting-week-2/train.csv")
test = pd.read_csv("../input/covid19-global-forecasting-week-2/test.csv")
sub = pd.read_csv("../input/covid19-global-forecasting-week-2/submission.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
199 | train | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
200 | train = train.append(test[test['Date']>'2020-03-31']) | No | 5 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
201 | train['Date'] = pd.to_datetime(train['Date'], format='%Y-%m-%d') | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
202 | train['day_dist'] = train['Date']-train['Date'].min() | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
203 | train['day_dist'] = train['day_dist'].dt.days | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
204 | print(train['Date'].max())
#print(val['Date'].max())
print(test['Date'].min())
print(test['Date'].max())
#print(test['Date'].max()-test['Date'].min()) | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
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