Merge pull request #92 from ExpandingMan/dataframesup

bumped dataframes version

.travis.yml

@@ -8,7 +8,7 @@ env:
     - PYTHON=Conda
 julia:
     - 1.0
-    - 1.4
+    - 1.5
     - nightly
 notifications:
     email: false
@@ -20,7 +20,7 @@ jobs:
   include:
     - if: branch = master OR tag IS present
       stage: "Documentation"
-      julia: 1.4
+      julia: 1.5
       os: linux
       # disable global before_script in order not to install Compose twice
       before_script:

Project.toml

@@ -1,6 +1,6 @@
 name = "ScikitLearn"
 uuid = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
-version = "0.6.2"
+version = "0.6.3"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
@@ -22,14 +22,14 @@ VersionParsing = "81def892-9a0e-5fdd-b105-ffc91e053289"
 [compat]
 Compat = "2.2, 3"
 Conda = "1.4"
-DataFrames = "0.20, 0.21"
+DataFrames = "0.20, 0.21, 0.22"
 IterTools = "1.2, 1.3"
 julia = "1"
 MacroTools = "0.5"
 Parameters = "0.12"
-PyCall = "1.91"
+PyCall = "1.92"
 ScikitLearnBase = "0.5"
-StatsBase = "0.32,0.33"
+StatsBase = "0.33"
 VersionParsing = "1.2"
 
 [extras]

README.md

@@ -1,4 +1,4 @@
- <a href="./examples/Classifier_Comparison_Julia.ipynb"><img src="./docs/example_images/Classifier_Comparison_Julia.png" alt="# Classifier Comparison (Julia classifiers)" width="170"> </a>  <a href="./examples/Clustering_Comparison.ipynb"><img src="./docs/example_images/Clustering_Comparison.png" alt="# Comparing different clustering algorithms on toy datasets" width="170"> </a>  <a href="./examples/Density_Estimation_Julia.ipynb"><img src="./docs/example_images/Density_Estimation_Julia.png" alt="# Density Estimation for a mixture of Gaussians (using GaussianMixtures.jl)" width="170"> </a>  <a href="./examples/Outlier_Detection.ipynb"><img src="./docs/example_images/Outlier_Detection.png" alt="# Outlier detection with several methods" width="170"> </a>  <a href="./examples/Plot_Kmeans_Digits.ipynb"><img src="./docs/example_images/Plot_Kmeans_Digits.png" alt="# A demo of K-Means clustering on the handwritten digits data" width="170"> </a>  <a href="./examples/RBM.ipynb"><img src="./docs/example_images/RBM.png" alt="# Restricted Boltzmann Machine features for digit classification" width="170"> </a>  <a href="./examples/Simple_1D_Kernel_Density.ipynb"><img src="./docs/example_images/Simple_1D_Kernel_Density.png" alt="# Simple 1D Kernel Density Estimation" width="170"> </a>  <a href="./examples/Text_Feature_Extraction.ipynb"><img src="./docs/example_images/Text_image.png" alt="# Sample pipeline for text feature extraction and evaluation" width="170"> </a>  <a href="./examples/Two_Class_Adaboost.ipynb"><img src="./docs/example_images/Two_Class_Adaboost.png" alt="# Two Class Adaboost" width="170"> </a>  <a href="./examples/Underfitting_vs_Overfitting.ipynb"><img src="./docs/example_images/Underfitting_vs_Overfitting.png" alt="# Underfitting vs. Overfitting" width="170"> </a> 
+ <a href="./examples/Classifier_Comparison_Julia.ipynb"><img src="./docs/example_images/Classifier_Comparison_Julia.png" alt="# Classifier Comparison (Julia classifiers)" width="170"> </a>  <a href="./examples/Clustering_Comparison.ipynb"><img src="./docs/example_images/Clustering_Comparison.png" alt="# Comparing different clustering algorithms on toy datasets" width="170"> </a>  <a href="./examples/Density_Estimation_Julia.ipynb"><img src="./docs/example_images/Density_Estimation_Julia.png" alt="# Density Estimation for a mixture of Gaussians (using GaussianMixtures.jl)" width="170"> </a>  <a href="./examples/Outlier_Detection.ipynb"><img src="./docs/example_images/Outlier_Detection.png" alt="# Outlier detection with several methods" width="170"> </a>  <a href="./examples/Plot_Kmeans_Digits.ipynb"><img src="./docs/example_images/Plot_Kmeans_Digits.png" alt="# A demo of K-Means clustering on the handwritten digits data" width="170"> </a>  <a href="./examples/RBM.ipynb"><img src="./docs/example_images/RBM.png" alt="# Restricted Boltzmann Machine features for digit classification" width="170"> </a>  <a href="./examples/Simple_1D_Kernel_Density.ipynb"><img src="./docs/example_images/Simple_1D_Kernel_Density.png" alt="# Simple 1D Kernel Density Estimation" width="170"> </a>  <a href="./examples/Text_Feature_Extraction.ipynb"><img src="./docs/example_images/Text_image.png" alt="# Sample pipeline for text feature extraction and evaluation" width="170"> </a>  <a href="./examples/Two_Class_Adaboost.ipynb"><img src="./docs/example_images/Two_Class_Adaboost.png" alt="# Two Class Adaboost" width="170"> </a>  <a href="./examples/Underfitting_vs_Overfitting.ipynb"><img src="./docs/example_images/Underfitting_vs_Overfitting.png" alt="# Underfitting vs. Overfitting" width="170"> </a>
 
 # ScikitLearn.jl
 [![Build Status](https://travis-ci.org/cstjean/ScikitLearn.jl.svg?branch=master)](https://travis-ci.org/cstjean/ScikitLearn.jl)

test/test_dataframes.jl

@@ -43,6 +43,6 @@ using ScikitLearn.CrossValidation: cross_val_score
 pipe = Pipelines.Pipeline([
      (:featurize, mapper),
      (:lm, LinearRegression())])
-round.(cross_val_score(pipe, data, data[:salary]), digits=2)
+round.(cross_val_score(pipe, data, data[:, :salary]), digits=2)
 
 end