# Copyright 2013 Philip N. Klein
from vec import Vec
from matutil import rowdict2mat

def read_training_data(fname, D=None):
    """Given a file in appropriate format, and given a set D of features,
    returns the pair (A, b) consisting of
    a P-by-D matrix A and a P-vector b,
    where P is a set of patient identification integers (IDs).

    For each patient ID p,
      - row p of A is the D-vector describing patient p's tissue sample,
      - entry p of b is +1 if patient p's tissue is malignant, and -1 if it is benign.

    The set D of features must be a subset of the features in the data (see text).
    """
    file = open(fname)
    params = ["radius", "texture", "perimeter","area","smoothness","compactness","concavity","concave points","symmetry","fractal dimension"];
    stats = ["(mean)", "(stderr)", "(worst)"]
    feature_labels = set([y+x for x in stats for y in params])
    feature_map = {params[i]+stats[j]:j*len(params)+i for i in range(len(params)) for j in range(len(stats))}
    if D is None: D = feature_labels
    feature_vectors = {}
    patient_diagnoses = {}
    for line in file:
        row = line.split(",")
        patient_ID = int(row[0])
        patient_diagnoses[patient_ID] = -1 if row[1]=='B' else +1
        feature_vectors[patient_ID] = Vec(D, {f:float(row[feature_map[f]+2]) for f in D})
    return rowdict2mat(feature_vectors), Vec(set(patient_diagnoses.keys()), patient_diagnoses)