nl2ml/graph/graph_v7.txt

{	
	"Hypothesis.statistical_test": ["shapiro(", "normaltest(", "anderson(", "pearsonr(", "spearmanr(", "kendalltau(", "chi2_contingency(", "adfuller(", "kpss(", "ttest_ind"],
	"Hyperparam_Tuning.choose_model_class": [".get_params().*?classifier"],
	"Hypothesis.pipeline_type_spec": ["YAML", "yaml"],

	"Environment.import_modules":  ["import \\w+ as \\w+", "import \\w+", "from \\w+ import \\w+", "from \\w+ import \\w+ as \\w+"],
	"Environment.set_options": ["os.environ", " pd.set_option"], 
	"Environment.get_options": [".get_config_var"],
 
	"Data_Extraction.load_from_url":["read_html"],
	"Data_Extraction.load_from_sql": ["read_sql"],
	"Data_Extraction.load_from_disk": ["read_fwf", "read_json", "read_clipboard", "read_excel", "read_hdf", "read_feather", "read_parquet", "read_orc", "read_msgpack", "read_stata", "read_sas", "read_spss", "read_pickle", "read_gbq"],
	"Data_Extraction.load_from_csv": ["read_csv"],

	"EDA.show_table": [".head", ".tail"],
	"EDA.show_table_attributes": [".columns", ".index"],
	"EDA.count_missing_values": [".isnull().sum"],
	"EDA.count_duplicates": ["len(\\w+)[  ]{0,1}-[  ]{0,1}len(\\w+).drop_duplicates"],
	"EDA.count_data_types": [".dtypes.value_counts("],
	"EDA.count_unique_values": [".nunique"],
	"EDA.get_unique_values": [".unique"],
	"EDA.show_shape": [".shape$", ".shape[.*]$"],

	"Data_Transform.create_dataframe": ["DataFrame"],
	"Data_Transform.remove_duplicates": ["drop_duplicates"],
	"Data_Transform.correct_missing_values": ["fillna", "SimpleImputer(missing_values="],
	"Data_Transform.normalization": ["normalize(", "StandartScaler(", "RobustScaler(", "MinMaxScaler(", "mean(\\w+)[ ]{0, 1}/[ ]{0, 1}std", "mean(\\w+))[ ]{0, 1}/[ ]{0, 1}np.std", ".mean()/[ ]{0, 1}[ ]{0, 1}/[ ]{0, 1}\\w+.std", "mean())/[ ]{0, 1}/[ ]{0, 1}\\w+.std"],
	"Data_Transform.data_type_conversions": ["to_numeric", "astype", "to_datetime", "to_timedelta", "infer_objects", "convert_dtypes"],
	"Data_Transform.randomize_order": [".random.shuffle"],
	"Data_Transform.split": [".split", "KFold("],
	"Data_Transform.filter": [".loc[\\w[ ]{0, 1}==]", ".loc[\\w[ ]{0, 1}>]", ".loc[\\w[ ]{0, 1}<]", ".iloc[\\w[ ]{0, 1}==]", ".iloc[\\w[ ]{0, 1}>]", ".iloc[\\w[ ]{0, 1}<]"],
	"Data_Transform.concatenate": ["concatenate"],
	"Data_Transform.drop_column": ["drop_column"],
	"Data_Transform.sort_values": ["sort_values"],
	"Data_Transform.feature_engineering": ["OneHotEncoder(", "Binarizer(", "FunctionTransformer(", "KBinsDiscretizer(",  "KernelCenterer(", "LabelBinarizer(", "LabelEncoder(", "MultiLabelBinarizer(", "MaxAbsScaler(", "MinMaxScaler(", "Normalizer(", "OrdinalEncoder(", "PolynomialFeatures(", "PowerTransformer(", "QuantileTransformer(", "binarize(", "label_binarize(", "power_transform(", ".apply("],
"Data_Transform.to_dummies":["get_dummies(", "add_dummy_feature("],
"Data_Transform.prepare_x_and_y": ["\\w+x, \\w+y ="],
"Data_Transform.categorify": ["Categorify"],
	"Data_Transform.augment":["augment", ".RandomFlip", ".RandomRotation", ".RandomHorizontalFlip", ".RandomColorJitter", "Augmentor.", "DeepAugment("],
 "Data_Transform.merge": [".merge"],
	"Data_Transform.groupby":[".groupby("],
	"Data_Transform.rename_columns":[".rename(columns", ".rename(.*columns.*)$", ".rename(.*axis=1)$"],

	"Model_Train.choose_model_class": ["LinearRegression(", "RandomForest(", "Ridge(", "RidgeCV(", "RidgeClassifier(", "RidgeClassifierCV(", "SGD(", "LogisticRegression(", "LogisticRegressionCV(", "SVC(", "SVR(", "Layer(", "XGboost(", "LGBM(", "Perceptron(", "KNeighborsRegressor(", "KNeighborsClassifier(", "SGDRegressor(", "ElasticNet(", "KMeans(", "AgglomerativeClustering(", "SpectralClustering(", "CategoricalNB(", "ComplementNB(", "DecisionTreeClassifier(", "DecisionTreeRegressor(", "Lasso(", "CatBoost(", "ElasticNetCV(", "Dense(", "Activation(", "Embedding(", "Masking(", "Lambda(", "Conv\\dD(", "SeparableConv\\dD(", "DepthwiseConv\\dD(", "Conv\\dDTranspose(", "MaxPooling\\dD(", "AveragePooling\\dD(", "GlobalPooling\\dD(", "GlobalAveragePooling\\dD(", "LSTM(", "GRU(", "RNN(", "SimpleRNN(", "Bidirectional(", "ConvLSTM2D(", "CategoryEncoding(", "CategoryCrossing(", "BatchNormalization(", "LayerNormalization(", "Dropout(", "SpatialDropout\\dD(", "GaussianDropout(", "GaussianNoise(", "ActivityRegularization(", "AlphaDropout(", "Attention(", "AdditiveAttention(", "Cropping\\dD(", "UpSampling\\dD(", "ZeroPadding\\dD(", "LocallyConnected\\dD(", "ReLU(", "Softmax(", "LeakyReLU(", "PReLU(", "ELU(", "ThresholdedReLU("],
	"Model_Train.train_model": [".fit("],
	"Model_Train.metric_computation": ["history[\"loss\"]", "history[\"accuracy\"]"],
	"Model_Train.predict": [".predict(", ".predict_proba("],
	"Model_Train.load_pretrained":["torch.load(", "load_tf_weights", "from_tf", ".from_pretrained("],
	"Model_Train.init_hyperparams": [".set_params("],

	"Model_Evaluation.compute_test_metric": ["KLDivergence class", "kl_divergence function", "MeanSquaredError", "MeanAbsoluteError", "mean_squared_error", "MeanAbsolutePercentageError", "MeanSquaredLogarithmicError", "CosineSimilarity", "mean_absolute_error", "mean_absolute_percentage_error", "mean_squared_logarithmic_error", "huber", "holdout"],
	"Model_Evaluation.predict_on_test": [".predict(test", ".predict(\\w+test", ".predict_proba(test", ".predict_proba(\\w+test"],

	"Model_Interpretation.get_coefficients": [".coef_"],

	"Hyperparam_Tuning.find_best_score": [ "best_score_"],
	"Hyperparam_Tuning.find_best_params": [".best_params_", ".best_params", "best_index_"],
	"Hyperparam_Tuning.find_best_model_class": ["for model\\w+ in", "for \\w+model in", "best_estimator_"],
	"Hyperparam_Tuning.train_on_grid": ["GridSearchCV("],
	"Hyperparam_Tuning.define_search_space": ["hp.choice(", "hp.uniform(", "hp.randint(", "hp.quniform(", "hp.loguniform(", "hp.qloguniform(", "hp.normal(", "hp.qnormal(", "hp.lognormal(", "hp.qlognormal("],
	"Hyperparam_Tuning.fit_one_cycle": ["fit_one_cycle("],

	"Visualization.learning_history": ["plot(\\w+history"], 
	"Visualization.distribution": ["distplot(", ".heatmap", ".hist"],
	"Visualization.wandb": ["wandb"],
	"Visualization.missing_values": ["msno."],
	"Visualization.plot_predictions": ["plot(.*test", "plot(.*pred"],

	"Data_Export.save_to_csv": ["to_csv"],

	"Production.send_to_prod_environment": ["kaggle competitions submit"],
	"Production.save_weights": ["save_weights("]
}