relevanceai.vector_tools.plot_text_theme_model

Module Contents

Classes

PlotTextThemeModel

Batch API client

Functions

build_and_plot_clusters(self, project: str, api_key: str, dataset_id: str, vector_fields: List[str], text_fields: List[str], upload_chunksize: int = 50, cluster_field: str = '_cluster_', embedding_dims: int = 2, dim_red_k: int = 800, n_epochs_without_progress: int = 100, language: str = 'english', max_doc_num: int = None, k: int = 10, alias: str = 'kmeans', lower: bool = True, remove_digit: bool = True, remove_punct: bool = True, remove_stop_words: bool = True, additional_stop_words: List[str] = [], cluster_representative_cnt: int = 3, plot_axis: str = 'off', figsize: Tuple[int, Ellipsis] = (20, 10), cmap: str = 'plasma', alpha: float = 0.2)

class relevanceai.vector_tools.plot_text_theme_model.PlotTextThemeModel(project: str, api_key: str, dataset_id: str, upload_chunksize: int = 50, cluster_field: str = '_cluster_', embedding_dims: int = 2, dim_red_k: int = 800, n_epochs_without_progress: int = 100, language: str = 'english')

Bases: relevanceai.api.client.BatchAPIClient, relevanceai.data_tools.base_text_processing.BaseTextProcessing, relevanceai.logger.LoguruLogger, doc_utils.DocUtils

Batch API client

_build_and_plot_clusters(self, vector_fields: List[str], text_fields: List[str], max_doc_num: int = None, k: int = 10, alias: str = 'kmeans', lower: bool = True, remove_digit: bool = True, remove_punct: bool = True, remove_stop_words: bool = True, additional_stop_words: List[str] = [], cluster_representative_cnt: int = 3, plot_axis: str = 'off', figsize: Tuple[int, Ellipsis] = (20, 10), cmap: str = 'plasma', alpha: float = 0.2)

_get_documents(self, vector_fields: List[str], text_fields: List[str], max_doc_num: int = None)

_batch_load_docs(self, fields: List[str], filters: List[dict] = [], page_size: int = 200, cursor: str = None)

_kmeans_clustering(self, docs: List[dict], vector_fields: List[str], k: int = 10, alias: str = 'kmeans')

static _get_fields_value_by_id(docs: List[dict], id: str, fields: List[str])

_get_cluster_datafield(self, docs: List[dict], vector_fields: List[str], text_fields: List[str], alias: str, lower: bool = True, remove_digit: bool = True, remove_punct: bool = True)

static _get_cluster_population(cluster_data: dict)

_get_cluster_word_freq(self, cluster_data: dict, remove_stop_words: bool = True, additional_stop_words: List[str] = [], cluster_representative_cnt: int = 3)

_dim_reduction(self, vector_data: List, embedding_dims=2, k=800, n_epochs_without_progress=100, run_fit=True)

_plot_clusters(self, docs: List[dict], dr_docs: List, centers: List[dict], cluster_data: dict, vector_fields: List[str], alias: str, cluster_representative_cnt: int, plot_axis: str = 'off', figsize: Tuple[int, Ellipsis] = (20, 10), cmap: str = 'plasma', alpha: float = 0.2)

relevanceai.vector_tools.plot_text_theme_model.build_and_plot_clusters(self, project: str, api_key: str, dataset_id: str, vector_fields: List[str], text_fields: List[str], upload_chunksize: int = 50, cluster_field: str = '_cluster_', embedding_dims: int = 2, dim_red_k: int = 800, n_epochs_without_progress: int = 100, language: str = 'english', max_doc_num: int = None, k: int = 10, alias: str = 'kmeans', lower: bool = True, remove_digit: bool = True, remove_punct: bool = True, remove_stop_words: bool = True, additional_stop_words: List[str] = [], cluster_representative_cnt: int = 3, plot_axis: str = 'off', figsize: Tuple[int, Ellipsis] = (20, 10), cmap: str = 'plasma', alpha: float = 0.2)