def train_test_combined(features_labels, fractions, train, test, mode):
    features_labels = np.array(features_labels, dtype=object)
    reg = Combined(random_state=1, mode=mode, n_jobs=N_JOBS)
    reg.fit(features_labels[train], fractions[train])
    rs = []
    for fs, frac in zip(features_labels[test], fractions[test]):
        fs = [f for f, _ in fs]
        r = reg.predict(fs, clip01=False)
        rs.append(r)
    return np.array(rs)
def train_test_combined(features_labels, fractions, train, test, mode):
    features_labels = np.array(features_labels, dtype=object)
    reg = Combined(random_state=1, mode=mode, n_jobs=N_JOBS)
    reg.fit(features_labels[train], fractions[train])
    rs = []
    for fs,frac in zip(features_labels[test], fractions[test]):
        fs = [f for f,_ in fs]
        r = reg.predict(fs, clip01=False)
        rs.append(r)
    return np.array(rs)
Example #3
0
def build_model(n_estimators, verbose=True, ofilename=None):
    if ofilename is None:
        ofilename = 'model.pkl.gz'
    fractions = []
    features = []
    images = list(listfiles())
    for im,g in images:
        protein,dna, rois = load_image(im)
        features.append([feats(protein,dna,rois) for name,feats in FEATURES])
        fractions.append(np.mean(rois > 0))
        if verbose:
            print("Computed features for image {} (out of {}).".format(len(fractions), len(images)))

    features= np.array(features, dtype=object)
    reg = Combined(random_state=1,  n_jobs=N_JOBS, n_estimators=n_estimators)
    if verbose:
        print("Fitting model...")
    reg.fit(features, fractions)

    if verbose:
        print("Saving model...")
    pickle.dump(reg, gzip.open('model.pkl.gz', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
    if verbose:
        print("Done.")
def build_model(n_estimators, verbose=True, ofilename=None):
    if ofilename is None:
        ofilename = 'model.pkl.gz'
    fractions = []
    features = []
    images = list(listfiles())
    for im,g in images:
        protein,dna, rois = load_image(im)
        features.append([feats(protein,dna,rois) for name,feats in FEATURES])
        fractions.append(np.mean(rois > 0))
        if verbose:
            print("Computed features for image {} (out of {}).".format(len(fractions), len(images)))

    features= np.array(features, dtype=object)
    reg = Combined(random_state=1,  n_jobs=N_JOBS, n_estimators=n_estimators)
    if verbose:
        print("Fitting model...")
    reg.fit(features, fractions)

    if verbose:
        print("Saving model...")
    pickle.dump(reg, gzip.open('model.pkl.gz', 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
    if verbose:
        print("Done.")