From 2d7a697fb2b753f7868b371ab2e06712bc6e8edc Mon Sep 17 00:00:00 2001 From: sanyhe Date: Fri, 15 Dec 2023 15:50:42 +0800 Subject: [PATCH] build: v0.4.0 --- geochemistrypi/_version.py | 2 +- geochemistrypi/data_mining/cli_pipeline.py | 2 +- .../dataset/InferenceData_Classification.xlsx | Bin 225291 -> 173418 bytes .../dataset/InferenceData_Regression.xlsx | Bin 44562 -> 21185 bytes .../data_mining/model/classification.py | 40 ++++++------ .../data_mining/model/clustering.py | 61 +++++++++++++++--- .../data_mining/model/decomposition.py | 1 + .../model/func/algo_classification/_common.py | 5 +- .../_sgd_classification.py | 11 +++- .../model/func/algo_clustering/_kmeans.py | 2 +- .../model/func/algo_decomposition/_tsne.py | 10 ++- .../data_mining/model/regression.py | 1 + pyproject.toml | 2 +- 13 files changed, 98 insertions(+), 39 deletions(-) diff --git a/geochemistrypi/_version.py b/geochemistrypi/_version.py index 493f7415..6a9beea8 100644 --- a/geochemistrypi/_version.py +++ b/geochemistrypi/_version.py @@ -1 +1 @@ -__version__ = "0.3.0" +__version__ = "0.4.0" diff --git a/geochemistrypi/data_mining/cli_pipeline.py b/geochemistrypi/data_mining/cli_pipeline.py index d6cb27d7..19d36dcb 100644 --- a/geochemistrypi/data_mining/cli_pipeline.py +++ b/geochemistrypi/data_mining/cli_pipeline.py @@ -265,7 +265,7 @@ def cli_pipeline(training_data_path: str, inference_data_path: Optional[str] = N del data_selected clear_output() else: - # if the selected data set doesn't need imputation, which means there are no missing values. + # If the selected data set doesn't need imputation, which means there are no missing values. imputation_config = {} data_selected_imputed = data_selected diff --git a/geochemistrypi/data_mining/data/dataset/InferenceData_Classification.xlsx b/geochemistrypi/data_mining/data/dataset/InferenceData_Classification.xlsx index 84b47f5a955c2594624bb9f1286b771a7e06cd3e..d9b96e9379dd5921e4574fca8c4c863471132c30 100644 GIT binary patch literal 173418 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b/geochemistrypi/data_mining/model/classification.py index 58a4093a..7e203fa0 100644 --- a/geochemistrypi/data_mining/model/classification.py +++ b/geochemistrypi/data_mining/model/classification.py @@ -56,6 +56,7 @@ def __init__(self) -> None: # These two attributes are used for the customized models of FLAML framework self.customized = False self.customized_name = None + self.mode = "Classification" @dispatch(object, object) def fit(self, X: pd.DataFrame, y: Optional[pd.DataFrame] = None) -> None: @@ -2995,7 +2996,8 @@ class SGDClassification(LinearWorkflowMixin, ClassificationWorkflowBase): """The automation workflow of using Stochastic Gradient Descent - SGD algorithm to make insightful products.""" name = "Stochastic Gradient Descent" - special_function = ["SGD Formula"] + # special_function = ["SGD Formula"] + special_function = [] def __init__( self, @@ -3315,25 +3317,25 @@ def manual_hyper_parameters(cls) -> Dict: @dispatch() def special_components(self, **kwargs) -> None: """Invoke all special application functions for this algorithms by Scikit-learn framework.""" - GEOPI_OUTPUT_ARTIFACTS_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_PATH") - self._show_formula( - coef=[self.model.coef_], - intercept=self.model.intercept_, - features_name=SGDClassification.X_train.columns, - algorithm_name=self.naming, - local_path=GEOPI_OUTPUT_ARTIFACTS_PATH, - mlflow_path="root", - ) + # GEOPI_OUTPUT_ARTIFACTS_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_PATH") + # self._show_formula( + # coef=[self.model.coef_], + # intercept=self.model.intercept_, + # features_name=SGDClassification.X_train.columns, + # algorithm_name=self.naming, + # local_path=GEOPI_OUTPUT_ARTIFACTS_PATH, + # mlflow_path="root", + # ) @dispatch(bool) def special_components(self, is_automl: bool = False, **kwargs) -> None: """Invoke all special application functions for this algorithms by FLAML framework.""" - GEOPI_OUTPUT_ARTIFACTS_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_PATH") - self._show_formula( - coef=self.auto_model.coef_, - intercept=self.auto_model.intercept_, - features_name=SGDClassification.X.columns, - algorithm_name=self.naming, - local_path=GEOPI_OUTPUT_ARTIFACTS_PATH, - mlflow_path="root", - ) + # GEOPI_OUTPUT_ARTIFACTS_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_PATH") + # self._show_formula( + # coef=self.auto_model.coef_, + # intercept=self.auto_model.intercept_, + # features_name=SGDClassification.X.columns, + # algorithm_name=self.naming, + # local_path=GEOPI_OUTPUT_ARTIFACTS_PATH, + # mlflow_path="root", + # ) diff --git a/geochemistrypi/data_mining/model/clustering.py b/geochemistrypi/data_mining/model/clustering.py index fc926640..93bdf85b 100644 --- a/geochemistrypi/data_mining/model/clustering.py +++ b/geochemistrypi/data_mining/model/clustering.py @@ -14,7 +14,7 @@ from ._base import WorkflowBase from .func.algo_clustering._common import plot_results, plot_silhouette_diagram, score from .func.algo_clustering._dbscan import dbscan_manual_hyper_parameters, dbscan_result_plot -from .func.algo_clustering._kmeans import kmeans_manual_hyper_parameters, scatter2d, scatter3d +from .func.algo_clustering._kmeans import kmeans_manual_hyper_parameters, plot_silhouette_diagram_kmeans, scatter2d, scatter3d class ClusteringWorkflowBase(WorkflowBase): @@ -25,6 +25,7 @@ class ClusteringWorkflowBase(WorkflowBase): def __init__(self): super().__init__() self.clustering_result = None + self.mode = "Clustering" def fit(self, X: pd.DataFrame, y: Optional[pd.DataFrame] = None) -> None: """Fit the model according to the given training data.""" @@ -93,14 +94,14 @@ def common_components(self) -> None: algorithm_name=self.naming, store_path=GEOPI_OUTPUT_METRICS_PATH, ) - self._plot_results( - data=self.X, - labels=self.clustering_result["clustering result"], - cluster_centers_=self.get_cluster_centers(), - algorithm_name=self.naming, - local_path=GEOPI_OUTPUT_ARTIFACTS_IMAGE_MODEL_OUTPUT_PATH, - mlflow_path=MLFLOW_ARTIFACT_IMAGE_MODEL_OUTPUT_PATH, - ) + # self._plot_results( + # data=self.X, + # labels=self.clustering_result["clustering result"], + # cluster_centers_=self.get_cluster_centers(), + # algorithm_name=self.naming, + # local_path=GEOPI_OUTPUT_ARTIFACTS_IMAGE_MODEL_OUTPUT_PATH, + # mlflow_path=MLFLOW_ARTIFACT_IMAGE_MODEL_OUTPUT_PATH, + # ) self._plot_silhouette_diagram( data=self.X, labels=self.clustering_result["clustering result"], @@ -226,6 +227,15 @@ def __init__( self.naming = KMeansClustering.name + def _get_inertia_scores(self, algorithm_name: str, store_path: str) -> None: + """Get the scores of the clustering result.""" + print("-----* KMeans Inertia Scores *-----") + print("Inertia Score: ", self.model.inertia_) + inertia_scores = {"Inertia Score": self.model.inertia_} + mlflow.log_metrics(inertia_scores) + inertia_scores_str = json.dumps(inertia_scores, indent=4) + save_text(inertia_scores_str, f"KMeans Inertia Scores - {algorithm_name}", store_path) + @classmethod def manual_hyper_parameters(cls) -> Dict: """Manual hyper-parameters specification.""" @@ -234,6 +244,25 @@ def manual_hyper_parameters(cls) -> Dict: clear_output() return hyper_parameters + @staticmethod + def _plot_silhouette_diagram_kmeans( + data: pd.DataFrame, + cluster_labels: pd.DataFrame, + cluster_centers_: np.ndarray, + n_clusters: int, + algorithm_name: str, + local_path: str, + mlflow_path: str, + ) -> None: + """Plot the silhouette diagram of the clustering result.""" + print("-----* KMeans's Silhouette Diagram *-----") + plot_silhouette_diagram_kmeans(data, cluster_labels, cluster_centers_, n_clusters, algorithm_name) + save_fig(f"KMeans's Silhouette Diagram - {algorithm_name}", local_path, mlflow_path) + data_with_labels = pd.concat([data, cluster_labels], axis=1) + save_data(data_with_labels, "KMeans's Silhouette Diagram - Data With Labels", local_path, mlflow_path) + cluster_center_data = pd.DataFrame(cluster_centers_, columns=data.columns) + save_data(cluster_center_data, "KMeans's Silhouette Diagram - Cluster Centers", local_path, mlflow_path) + @staticmethod def _scatter2d(data: pd.DataFrame, cluster_labels: pd.DataFrame, algorithm_name: str, local_path: str, mlflow_path: str) -> None: """Plot the two-dimensional diagram of the clustering result.""" @@ -254,7 +283,21 @@ def _scatter3d(data: pd.DataFrame, cluster_labels: pd.DataFrame, algorithm_name: def special_components(self, **kwargs: Union[Dict, np.ndarray, int]) -> None: """Invoke all special application functions for this algorithms by Scikit-learn framework.""" + GEOPI_OUTPUT_METRICS_PATH = os.getenv("GEOPI_OUTPUT_METRICS_PATH") GEOPI_OUTPUT_ARTIFACTS_IMAGE_MODEL_OUTPUT_PATH = os.getenv("GEOPI_OUTPUT_ARTIFACTS_IMAGE_MODEL_OUTPUT_PATH") + self._get_inertia_scores( + algorithm_name=self.naming, + store_path=GEOPI_OUTPUT_METRICS_PATH, + ) + self._plot_silhouette_diagram_kmeans( + data=self.X, + cluster_labels=self.clustering_result["clustering result"], + cluster_centers_=self.get_cluster_centers(), + n_clusters=self.n_clusters, + algorithm_name=self.naming, + local_path=GEOPI_OUTPUT_ARTIFACTS_IMAGE_MODEL_OUTPUT_PATH, + mlflow_path=MLFLOW_ARTIFACT_IMAGE_MODEL_OUTPUT_PATH, + ) # Draw graphs when the number of principal components > 3 if self.X.shape[1] >= 3: diff --git a/geochemistrypi/data_mining/model/decomposition.py b/geochemistrypi/data_mining/model/decomposition.py index 63df5789..d9490456 100644 --- a/geochemistrypi/data_mining/model/decomposition.py +++ b/geochemistrypi/data_mining/model/decomposition.py @@ -26,6 +26,7 @@ def __init__(self) -> None: # the extra attributes that decomposition algorithm needs self.X_reduced = None + self.mode = "Decomposition" def fit(self, X: pd.DataFrame, y: Optional[pd.DataFrame] = None) -> None: """Fit the model.""" diff --git a/geochemistrypi/data_mining/model/func/algo_classification/_common.py b/geochemistrypi/data_mining/model/func/algo_classification/_common.py index 945429bb..94d05535 100644 --- a/geochemistrypi/data_mining/model/func/algo_classification/_common.py +++ b/geochemistrypi/data_mining/model/func/algo_classification/_common.py @@ -6,8 +6,6 @@ import mlflow import numpy as np import pandas as pd -from data_mining.constants import CALCULATION_METHOD_OPTION, SECTION -from data_mining.data.data_readiness import limit_num_input, num2option, num_input from imblearn.over_sampling import RandomOverSampler from imblearn.pipeline import Pipeline from imblearn.under_sampling import RandomUnderSampler @@ -16,6 +14,9 @@ from sklearn.model_selection import cross_validate from sklearn.preprocessing import LabelEncoder +from ....constants import CALCULATION_METHOD_OPTION, SECTION +from ....data.data_readiness import limit_num_input, num2option, num_input + def score(y_true: pd.DataFrame, y_predict: pd.DataFrame) -> tuple[str, Dict]: """Calculate the scores of the classification model. diff --git a/geochemistrypi/data_mining/model/func/algo_classification/_sgd_classification.py b/geochemistrypi/data_mining/model/func/algo_classification/_sgd_classification.py index 2902a102..f0d49e47 100644 --- a/geochemistrypi/data_mining/model/func/algo_classification/_sgd_classification.py +++ b/geochemistrypi/data_mining/model/func/algo_classification/_sgd_classification.py @@ -58,9 +58,14 @@ def sgd_classificaiton_manual_hyper_parameters() -> Dict: early_stopping = bool_input(SECTION[2]) print("Validation Fraction: The proportion of training data to set aside as validation set for early stopping.") - print("A good starting value could be between 0.000001 and 1, such as 0.1. The default is 0.1.") - validation_fraction = float_input(0.1, SECTION[2], "@Validation Fraction: ") - + print("It must be in range (0, 1). A good starting value could be between 0.000001 and 1, such as 0.1. The default is 0.1.") + is_valid = False + while not is_valid: + validation_fraction = float_input(0.1, SECTION[2], "@Validation Fraction: ") + if 0 < validation_fraction < 1: + is_valid = True + else: + print("The validation fraction must be in range (0, 1).") print("Number of Iterations With No Improvement: Number of iterations with no improvement to wait before stopping fitting.") print("A good starting value could be between 1 and maximum number of iterations, such as 5. The default is 5.") n_iter_no_change = num_input(SECTION[2], "@Iterations With No Improvement: ") diff --git a/geochemistrypi/data_mining/model/func/algo_clustering/_kmeans.py b/geochemistrypi/data_mining/model/func/algo_clustering/_kmeans.py index e15c3219..5e3b2654 100644 --- a/geochemistrypi/data_mining/model/func/algo_clustering/_kmeans.py +++ b/geochemistrypi/data_mining/model/func/algo_clustering/_kmeans.py @@ -40,7 +40,7 @@ def kmeans_manual_hyper_parameters() -> Dict: return hyper_parameters -def plot_silhouette_diagram(data: pd.DataFrame, cluster_labels: pd.DataFrame, cluster_centers_: np.ndarray, n_clusters: int, algorithm_name: str) -> None: +def plot_silhouette_diagram_kmeans(data: pd.DataFrame, cluster_labels: pd.DataFrame, cluster_centers_: np.ndarray, n_clusters: int, algorithm_name: str) -> None: """ Draw the silhouette diagram for analysis. diff --git a/geochemistrypi/data_mining/model/func/algo_decomposition/_tsne.py b/geochemistrypi/data_mining/model/func/algo_decomposition/_tsne.py index 96a439ca..45cc873b 100644 --- a/geochemistrypi/data_mining/model/func/algo_decomposition/_tsne.py +++ b/geochemistrypi/data_mining/model/func/algo_decomposition/_tsne.py @@ -24,8 +24,14 @@ def tsne_manual_hyper_parameters() -> Dict: print("Please specify the learning rate. A good starting range could be between 10 and 1000, such as 200.") learning_rate = float_input(200, SECTION[2], "Learning Rate: ") print("Number of Iterations: This parameter controls how many iterations the optimization will run for.") - print("Please specify the number of iterations. A good starting range could be between 250 and 1000, such as 500.") - n_iter = num_input(SECTION[2], "Number of Iterations: ") + print("Please specify the number of iterations. A good starting range could be between 250 and 1000, such as 500. The minimum is 250.") + is_valid = False + while not is_valid: + n_iter = num_input(SECTION[2], "Number of Iterations: ") + if n_iter >= 250: + is_valid = True + else: + print("Please enter a number greater than or equal to 250.") print("Early Exaggeration: This parameter controls how tight natural clusters in the original space are in the embedded space and how much space will be between them.") print("Please specify the early exaggeration. A good starting range could be between 5 and 50, such as 12.") early_exaggeration = float_input(12, SECTION[2], "Early Exaggeration: ") diff --git a/geochemistrypi/data_mining/model/regression.py b/geochemistrypi/data_mining/model/regression.py index 59e06381..63848ce2 100644 --- a/geochemistrypi/data_mining/model/regression.py +++ b/geochemistrypi/data_mining/model/regression.py @@ -47,6 +47,7 @@ def __init__(self) -> None: # These two attributes are used for the customized models of FLAML framework self.customized = False self.customized_name = None + self.mode = "Regression" @dispatch(object, object) def fit(self, X: pd.DataFrame, y: Optional[pd.DataFrame] = None) -> None: diff --git a/pyproject.toml b/pyproject.toml index dc780518..c573f98a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "geochemistrypi" -version = "0.4.0.dev2" +version = "0.4.0" authors = [ { name="Can He", email="sanyhew1097618435@163.com" }, ]