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    "info": {
        "author": "Eligijus Bujokas",
        "author_email": "eligijus@sixads.net",
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            "License :: OSI Approved :: MIT License",
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        "description": "# sixadsml\n\nPackage that is used by the SixAds data science department. To know more about sixads,\nvisit https://sixads.net/.\n\nThe github link for this package is https://bitbucket.org/eligijus112/sixadsml/src/master/\n\nInstallation\n\nIn anaconda prompt type (windows users):\n\n```console\ncurl https://bootstrap.pypa.io/get-pip.py -o get-pip.py\npython get-pip.py\npip install SixAdsDS\n```\n\n# sixadsml.clean_text\n\nFunctions for text preprocesing/summarizing.\nA common way to use these functions is to combine them into a pipeline which the input is a list containing strings.\n\n## lemmatize_word\n```python\nlemmatize_word(string_list, engine=<WordNetLemmatizer>)\n```\n\nLemmatize words using one of the WordNet engines\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\n**engine** : WordNetLemmatizer() (default)\n    An object from the nltk.stem.wodnet library\n\nReturns\n-------\n\n    List with the same length as *string_list* where each word in each\n    string is lemmatized\n\n\n## to_str\n```python\nto_str(string_list)\n```\n\nConverts every list element to str type\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every list element\n    is converted to a str type object\n\n\n## rm_short_words\n```python\nrm_short_words(string_list, lower_bound=1, upper_bound=2)\n```\n\nRemoves characters that are in the range of *lower_bound* and *upper_bound*\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\n**lower_bound**: int\n    Integer indicating the lower bound of a character length\n\n**upper_bound**: int\n    Integer indicating the upper bound of a character length\n\nReturns\n-------\n\n    List with the same length as *string_list* where every character that is\n    split by whitespace is removed if it has a length in the range\n    [lower_bound, upper_bound]\n\nExamples\n--------\n>>> string_list = ['python is awesome', 'R is good as well']\n\n>>> rm_short_words(string_list)\n\n>>> rm_short_words(string_list, 4, 5)\n\n## to_single\n```python\nto_single(string_list)\n```\n\nConverts every word in string_list to it's singular form.\n\nParameters\n----------\n\n**string_list**: list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every word is converted\n    to singular form\n\n## to_lower\n```python\nto_lower(string_list)\n```\n\nMakes every word in the string_list lowercase\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every word is converted\n    to lowercase\n\n## rm_stop_words\n```python\nrm_stop_words(string_list)\n```\n\nRemoves stop words using the nltk stopwords module.\n\nParameters\n----------\n\n **string_list** : list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every string is without\n    stopwords\n\n## rm_punctuations\n```python\nrm_punctuations(string_list)\n```\n\nRemoves punctuations and other special characters from a string list\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every string is without\n    punctuations and other special characters\n\n## rm_digits\n```python\nrm_digits(string_list)\n```\n\nRemoves digits from a string list\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every string is without\n    digits\n\n## stem_words\n```python\nstem_words(string_list, stemmer=<nltk.stem.snowball.SnowballStemmer object at 0x000001C95E8BCA20>)\n```\n\nA function to stemm the words in a given string vector\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\n**stemmer** : word stemmer from nltk.stem library;\n     nltk.stem.SnowballStemmer('english') default\n\nReturns\n-------\n\n    List with the same length as *string_list* where every character is stemmed\n\n## clean_ws\n```python\nclean_ws(string_list)\n```\n\nCleans one or more whitespaces\n\nParameters\n----------\n string_list : list\n    List which stores strings\n\nReturns\n-------\n\n    List with the same length as *string_list* where every string has only\n    one or less whitespace\n\n\n## build_vocab\n```python\nbuild_vocab(string_list, verbose=True)\n```\n\nA function that creates a term frequency vocabulary from the text\n\nParameters\n----------\n\n**string_list** : list\n    List which stores strings\n\n**verbose** : boolean; default=True\n     Whether to show the timing of the for loop\n\nReturns\n-------\n\n    dictionary that each key is a unique term in the string_list and\n    the key value is the number of times a certain term appeared in the\n    string\n\nExample\n-------\n>>> string_list = string_list = ['python is awesome', 'R is awesome as well']\n\n>>> build_vocab(string_list)\n\n# sixadsml.images\n\nFunctions to preproces images from the web or a local machine\n\n## img_read_url\n```python\nimg_read_url(url, h=256, w=256, to_grey=False, timeout=2)\n```\n\nReturns an image via an url\n\nParameters\n----------\n\n**url** : string\n    url (in a string format)\n\n**h**: int\n    Desired height of the returned image (px)\n\n**w**: int\n    Desired width of the returned image (px)\n\n**to_grey**: bool\n    should the image be returned in greyscale?\n\n**timeout**: int\n    maximum wait time before dropping the request\n\nReturns\n-------\n\n    A numpy array width dimensions (h, w, 3) or (h, w, 1) if to_grey=True\n\n## img_read_url_PIL\n```python\nimg_read_url_PIL(url, h=256, w=256, timeout=2)\n```\n\nReturns an image via an url (using PIL framework)\n\nParameters\n----------\n\n**url** : string\n    url (in a string format)\n\n**h**: int\n    Desired height of the returned image (px)\n\n**w**: int\n    Desired width of the returned image (px)\n\n**timeout**: int\n    maximum wait time before dropping the request\n\nReturns\n-------\n    PIL.Image.Image\n\n## img_read\n```python\nimg_read(path, h=256, w=256, to_grey=False)\n```\n\nReads an image from the local machine\n\nParameters\n----------\n\n**path** : string\n    path to image on a local machine\n\n**h**: int\n    Desired height of the returned image (px)\n\n**w**: int\n    Desired width of the returned image (px)\n\n**to_grey**: bool\n    Should the image be returned in greyscale?\n\nReturns\n-------\n\n    Numpy array width dimensions (h, w, 3) or (h, w, 1) if to_grey=True\n\n## return_image_hist\n```python\nreturn_image_hist(image, no_bins_per_channel=10, normalize=False)\n```\n\nFunction to get the histogram of the colours in a photo\n\nParameters\n----------\n\n**image** : numpy ndarray\n    A numpy array with the shape (x, y, 3)\n\n**no_bins_per_channel**: int\n    How many bins should a histgoram have for each channel of colors\n\n**normalize** : bool\n    Should the coordinates add up to 1?\n\nReturns\n-------\n\n    A list of size 3 * no_bins_per_channel representing the distribution\n    of colors in the image\n\n\n# sixadsml.utility\n\nUtility functions\n\n## make_connection\n```python\nmake_connection(specs)\n```\n\nCreates a connection based on the information in the *specs*. Ussually,\nthe *specs* dictionary is the output of the *read_yaml* function\n\nParameters\n----------\n\n**specs** : dictionary\n    A dictionary that stores the user, password, host and db keys\n\nReturns\n-------\n\n    An sql_alchemy connection object\n\n## exec_file\n```python\nexec_file(file, add_params=None)\n```\n\nExecutes a file with the .py extension\n\nParameters\n----------\n\n**file**: string\n    path to the python file\n\n**add_params**:\n    additional parameters that are used in the file that is beeing executed\n\nReturns\n-------\n\n    Whatever output the executable file outputs\n\n## read_yaml\n```python\nread_yaml(file)\n```\n\nReads a .yml or .yaml file\n\nParameters\n----------\n\n**path**: string\n\n    path to the .yml or .yaml files\n\nReturns\n-------\n\n    Dictionary with the .yml or .yaml file contents\n\n## chunks_of_n\n```python\nchunks_of_n(l, n)\n```\n\nSplits a list into n equal sizes\n\nParameters\n----------\n\n**l**: list\n\n**n**: int\n\nReturns\n-------\n\n    A list of size *n* with the items of *l* splited equaly\n\n## unique\n```python\nunique(l)\n```\n\nA handy function to return unique elements of a list or a numpy array\n\nParameters\n----------\n\n**l** : list or array\n\nReturns\n-------\n\n    A list or array containing unique elements of l\n\n# sixadsml.sql_utility\n\nFunctions to deal with downloading and writting data to the database\n\n## Get_sql\n```python\nGet_sql(self, /, *args, **kwargs)\n```\n\nClass that deals with downloading data\n\n### get_google_tree\n```python\nGet_sql.get_google_tree(connection)\n```\n\nFunction to download the google taxonomy tree from the sixads database\n\nParameters\n----------\n\n**connection**: sql_alchemy connection object\n\nReturns\n-------\n\n    A pandas dataframe\n\n### get_data\n```python\nGet_sql.get_data(connection, select_part, from_part, where_part='')\n```\n\nFunction that construcs a query from the given parts and executes it\n\nParameters\n----------\n\n**select_part**: list\n    list of strings identifying the desired columns\n\n**from_part**: string\n    the table name\n\n**where_part**: string\n    additional constaints\n\nReturns\n-------\n\n    A pandas dataframe\n\n## Write_sql\n```python\nWrite_sql(self, /, *args, **kwargs)\n```\n\nClass that deals with writting data\n\n### write_to_table\n```python\nWrite_sql.write_to_table(specs, table, data, if_exists='replace')\n```\n\nWrites data to the desired table\n\nParameters\n---------\n\n**specs**: dictionary\n    Must contain the keys user, password, host and db\n\n**table**: string\n    A string refering to the table which we want to write to\n\n**data**: pandas dataframe\n    Data which we want to write to the table\n\n**if_exists**: string\n    What to do if the table already exists. Possible string values:\n    'replace', 'append', 'fail'\n\n\n# sixadsml.embeddings\n\nClass for dealing with word embeddings\n\n## load_from_text\n```python\nload_from_text(path)\n```\n\nReads the word embeddings from a txt documents and saves it as a dictionary\n\nParameters\n----------\n\n**path**: string\n    path to a txt document containing the word embeddings\n\nReturns\n-------\n\n    A dictionary where the key values are individual words and the\n    values are the vectors\n\n## tokenize_text\n```python\ntokenize_text(string_list, max_features, max_len)\n```\n\nCreates a tokenizer from a given text list\n\nParameters\n----------\n\n**string_list**: list\n    List containing strings\n\n**max_features**: int\n    The maximum number of unique words that the tokenizer saves in memory\n\n**max_len**: int\n    The length of the vector into which all elements of *string_list* will\n    be converted to.\n\nReturns\n-------\n\n    A tuple of the tokenized text and the fitted tokenizer for future use.\n    The first element of the tuple is an array of shape (len(*string_list*), max_len)\n\n## create_embedding_matrix\n```python\ncreate_embedding_matrix(embeddings, tokenizer, max_features, embed_size=300)\n```\n\nFunction to create the embedding matrix to use in neural networks. 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