SYMBOL INDEX (60 symbols across 17 files)

FILE: ch04/code/ControlStructures.py
  function get_random_int_op (line 22) | def get_random_int_op(minimum: int, maximum: int) -> int:
  function process_small_op (line 31) | def process_small_op(data: int):
  function process_medium_op (line 38) | def process_medium_op(data: int):
  function process_large_op (line 45) | def process_large_op(data: int):
  function conditional_pipeline (line 58) | def conditional_pipeline():

FILE: ch04/code/Lightweight Pipeline.py
  function add (line 22) | def add(a: float, b: float) -> float:
  function my_divmod (line 40) | def my_divmod(
  function calc_pipeline (line 80) | def calc_pipeline(

FILE: ch04/code/RecommenderPipeline.py
  function recommender_pipeline (line 46) | def recommender_pipeline():

FILE: ch06/MLflow.py
  function evaluation_model (line 120) | def evaluation_model(y_test, y_pred):
  function train_knnmodel (line 142) | def train_knnmodel(parameters, inputs, tags, log=False):
  function train_mlpmodel (line 211) | def train_mlpmodel(parameters, inputs, tags, log=False):
  class PTG (line 286) | class PTG:
    method __init__ (line 287) | def __init__(self, thresholds_x0, thresholds_a, thresholds_b):
    method get_ptgmodel (line 292) | def get_ptgmodel(self, x, a, b, x0):
    method fit (line 296) | def fit(self, dfx, y):
    method predict (line 317) | def predict(self, dfx):
  function train_ptgmodel (line 328) | def train_ptgmodel(parameters, inputs, tags, log=False):

FILE: ch2/query-endpoint.py
  function download_mnist (line 25) | def download_mnist():
  function gen_image (line 29) | def gen_image(arr):

FILE: ch2_seldon_examples/tf_mnist_no_seldon_pipeline.py
  function mnist_pipeline (line 29) | def mnist_pipeline(gcs_bucket=None,

FILE: ch2_seldon_examples/train_pipeline.py
  function mnist_train_pipeline (line 11) | def mnist_train_pipeline(docker_org="index.docker.io/seldonio",

FILE: ch9/ctscans/process-dicoms-into-vectors/src/program.py
  function create_3d_matrix (line 25) | def create_3d_matrix(path):
  function upload_blob (line 53) | def upload_blob(bucket_name, source_file_name, destination_blob_name):

FILE: ch9/ctscans/visualize-basis-vectors/src/program.py
  function read_mahout_drm (line 19) | def read_mahout_drm(path):
  function plot_3d_matrix (line 35) | def plot_3d_matrix(img3d, img_shape, ax_aspect, sag_aspect, cor_aspect):
  function plot_2_3d_matrices (line 51) | def plot_2_3d_matrices(img1, img2, aspect, slice, cmap):
  function upload_blob (line 61) | def upload_blob(bucket_name, source_file_name, destination_blob_name):
  function download_folder (line 77) | def download_folder(bucket_name='your-bucket-name',

FILE: data-extraction/python-notebook/MailingListDataPrep.py
  function scrapeMailArchives (line 36) | def scrapeMailArchives(mailingList: str, year: int, month: int):
  function extract_links (line 69) | def extract_links(body):
  function extract_domains (line 75) | def extract_domains(links):
  function contains_python_stack_trace (line 91) | def contains_python_stack_trace(body):
  function contains_probably_java_stack_trace (line 95) | def contains_probably_java_stack_trace(body):
  function contains_exception_in_task (line 106) | def contains_exception_in_task(body):
  function makeDomainsAList (line 147) | def makeDomainsAList(d):
  function download_data (line 189) | def download_data(year: int) -> str:
  function download_tld_data (line 252) | def download_tld_data() -> str:
  function clean_data (line 278) | def clean_data(input_path: str) -> str:
  function prepare_features (line 313) | def prepare_features(input_path: str, tld_info_path: str):
  function my_pipeline_mini (line 430) | def my_pipeline_mini(year: int):
  function my_pipeline2 (line 466) | def my_pipeline2(year: int):
  function train_func (line 535) | def train_func(input_path: String):

FILE: data-extraction/python-spark-notebook/SparkMailingListForKF.py
  function download_emails (line 88) | def download_emails(date):
  function extract_date_from_email_datefield (line 165) | def extract_date_from_email_datefield(datefield):
  function is_ok (line 202) | def is_ok(post):

FILE: data-extraction/python-spark/LaunchSparkJobs.py
  function local_pipeline (line 61) | def local_pipeline():

FILE: data-extraction/spark-hello-world/hello_world_pipeline.py
  function spark_hello_world_pipeline (line 11) | def spark_hello_world_pipeline(jar_location="gcs://....", tf_job_image="...

FILE: data-extraction/tfx/TFDV.py
  function pipeline_with_dl (line 42) | def pipeline_with_dl():
  function tfdv_pipeline (line 69) | def tfdv_pipeline():
  function tfx_pipeline (line 145) | def tfx_pipeline():

FILE: feature-prep/spark/SparkMailingListFeaturePrep.py
  function extract_links (line 133) | def extract_links(body):
  function extract_domains (line 140) | def extract_domains(links):
  function contains_python_stack_trace (line 156) | def contains_python_stack_trace(body):
  function contains_probably_java_stack_trace (line 160) | def contains_probably_java_stack_trace(body):
  function contains_exception_in_task (line 171) | def contains_exception_in_task(body):

FILE: feature-prep/tft/transform.py
  function preprocessing_fn (line 10) | def preprocessing_fn(inputs):

FILE: recommender/Recommender_Kubeflow.py
  class DeepCollaborativeFiltering (line 233) | class DeepCollaborativeFiltering(Model):
    method __init__ (line 234) | def __init__(self, n_customers, n_products, n_factors, p_dropout=0.2):
    method rate (line 256) | def rate(self, customer_idxs, product_idxs):