regen images, readme has no i+1, use toy_classification dataset

README.md

@@ -93,7 +93,7 @@ fig, axs = plt.subplots(1, 3)
 titles = ['year', 'age', 'education']
 
 for i, ax in enumerate(axs):
-    pdep, confi = gam.partial_dependence(XX, feature=i+1, width=.95)
+    pdep, confi = gam.partial_dependence(XX, feature=i, width=.95)
 
     ax.plot(XX[:, i], pdep)
     ax.plot(XX[:, i], *confi, c='r', ls='--')
@@ -179,7 +179,7 @@ fig, axs = plt.subplots(1, 3)
 titles = ['student', 'balance', 'income']
 
 for i, ax in enumerate(axs):
-    pdep, confi = gam.partial_dependence(XX, feature=i+1, width=.95)
+    pdep, confi = gam.partial_dependence(XX, feature=i, width=.95)
 
     ax.plot(XX[:, i], pdep)
     ax.plot(XX[:, i], confi[0], c='r', ls='--')
@@ -235,7 +235,7 @@ gam = PoissonGAM().gridsearch(X, y)
 
 plt.hist(faithful(return_X_y=False)['eruptions'], bins=200, color='k');
 plt.plot(X, gam.predict(X), color='r')
-plt.title('Lam: {0:.2f}'.format(gam.lam))
+plt.title('Best Lambda: {0:.2f}'.format(gam.lam))
 ```
 <img src=imgs/pygam_poisson.png>
 
@@ -322,6 +322,9 @@ pyGAM is intuitive, modular, and adheres to a familiar API:
 
 ```python
 from pygam import LogisticGAM
+from pygam.datasets import toy_classification
+
+X, y = toy_classification(return_X_y=True)
 
 gam = LogisticGAM()
 gam.fit(X, y)

gen_imgs.py

@@ -8,6 +8,7 @@
 
 from pygam import *
 from pygam.utils import generate_X_grid
+from pygam.datasets import hepatitis, wage, faithful, mcycle, trees, default, cake, toy_classification
 
 np.random.seed(420)
 fontP = FontProperties()
@@ -20,60 +21,6 @@
 # monotonic increasing, concave constraint on hep data
 # prediction intervals on motorcycle data
 
-
-def hepatitis():
-    hep = pd.read_csv('datasets/hepatitis_A_bulgaria.csv').astype(float)
-    # eliminate 0/0
-    mask = (hep.total > 0).values
-    hep = hep[mask]
-    X = hep.age.values
-    y = hep.hepatitis_A_positive.values / hep.total.values
-    return X, y
-
-def mcycle():
-    motor = pd.read_csv('datasets/mcycle.csv', index_col=0)
-    X = motor.times.values[:,None]
-    y = motor.accel
-    return X, y
-
-def faithful():
-    faithful = pd.read_csv('datasets/faithful.csv', index_col=0)
-    y, xx, _ = plt.hist(faithful.values[:,0], bins=200, color='k')
-    X = xx[:-1] + np.diff(xx)/2 # get midpoints of bins
-    return X, y
-
-def wage():
-    wage = pd.read_csv('datasets/wage.csv', index_col=0)
-    X = wage[['year', 'age', 'education']].values
-    y = wage['wage'].values
-
-    # change education level to integers
-    X[:,-1] = np.unique(X[:,-1], return_inverse=True)[1]
-    return X, y
-
-def trees():
-    trees = pd.read_csv('datasets/trees.csv', index_col=0)
-    y = trees.Volume
-    X = trees[['Girth', 'Height']]
-    return X, y
-
-def default():
-    default = pd.read_csv('datasets/default.csv', index_col=0)
-    default = default.values
-    default[:,0] = np.unique(default[:,0], return_inverse=True)[1]
-    default[:,1] = np.unique(default[:,1], return_inverse=True)[1]
-    X = default[:,1:]
-    y = default[:,0]
-    return X, y
-
-def cake():
-    cake = pd.read_csv('datasets/cake.csv', index_col=0)
-    X = cake[['recipe', 'replicate', 'temperature']].values
-    X[:,0] = np.unique(cake.values[:,1], return_inverse=True)[1]
-    X[:,1] -= 1
-    y = cake['angle'].values
-    return X, y
-
 def gen_basis_fns():
     X, y = hepatitis()
     gam = LinearGAM(lam=.6, fit_intercept=False).fit(X, y)
@@ -109,10 +56,10 @@ def faithful_data_poisson():
     gam = PoissonGAM().gridsearch(X, y)
 
     plt.figure()
-    plt.bar(X, y, width=np.diff(X)[0], color='k')
+    plt.hist(faithful(return_X_y=False)['eruptions'], bins=200, color='k');
 
     plt.plot(X, gam.predict(X), color='r')
-    plt.title('Best Lamba: {0:.2f}'.format(gam.lam))
+    plt.title('Best Lambda: {0:.2f}'.format(gam.lam))
     plt.savefig('imgs/pygam_poisson.png', dpi=300)
 
 def single_data_linear():
@@ -125,7 +72,7 @@ def single_data_linear():
     plt.figure()
     plt.scatter(X, y, facecolor='gray', edgecolors='none')
     plt.plot(X, gam.predict(X), color='r')
-    plt.title('Best Lamba: {0:.2f}'.format(gam.lam))
+    plt.title('Best Lambda: {0:.2f}'.format(gam.lam))
     plt.savefig('imgs/pygam_single_pred_linear.png', dpi=300)
 
 def mcycle_data_linear():
@@ -172,8 +119,8 @@ def wage_data_linear():
 
     titles = ['year', 'age', 'education']
     for i, ax in enumerate(axs):
-        ax.plot(XX[:, i], gam.partial_dependence(XX, feature=i+1))
-        ax.plot(XX[:, i], *gam.partial_dependence(XX, feature=i+1, width=.95)[1],
+        ax.plot(XX[:, i], gam.partial_dependence(XX, feature=i))
+        ax.plot(XX[:, i], *gam.partial_dependence(XX, feature=i, width=.95)[1],
                 c='r', ls='--')
         if i == 0:
             ax.set_ylim(-30,30);
@@ -195,8 +142,8 @@ def default_data_logistic(n=500):
 
     titles = ['student', 'balance', 'income']
     for i, ax in enumerate(axs):
-        ax.plot(XX[:, i], gam.partial_dependence(XX, feature=i+1))
-        ax.plot(XX[:, i], *gam.partial_dependence(XX, feature=i+1, width=.95)[1],
+        ax.plot(XX[:, i], gam.partial_dependence(XX, feature=i))
+        ax.plot(XX[:, i], *gam.partial_dependence(XX, feature=i, width=.95)[1],
                 c='r', ls='--')
         ax.set_title(titles[i])
 
@@ -281,24 +228,17 @@ def trees_data_custom():
 #     plt.savefig('imgs/pygam_lambda_gridsearch.png', dpi=300)
 
 
-def gen_multi_data(n=200):
+def gen_multi_data(n=5000):
     """
     multivariate Logistic problem
     """
-    n = 5000
-    x = np.random.rand(n,5) * 10 - 5
-    cat = np.random.randint(0,4, n)
-    x = np.c_[x, cat]
-    log_odds = (-0.5*x[:,0]**2) + 5 +(-0.5*x[:,1]**2) + np.mod(x[:,-1], 2)*-30
-    p = 1/(1+np.exp(-log_odds)).squeeze()
-
-    obs = (np.random.rand(len(x)) < p).astype(np.int)
+    X, y = toy_classification(return_X_y=True, n=n)
 
     lgam = LogisticGAM()
-    lgam.fit(x, obs)
+    lgam.fit(X, y)
 
     plt.figure()
-    plt.plot(lgam.partial_dependence(np.sort(x, axis=0)))
+    plt.plot(lgam.partial_dependence(np.sort(X, axis=0)))
     plt.savefig('imgs/pygam_multi_pdep.png', dpi=300)
 
     plt.figure()