Set Ray dependency to 0.5.2

.travis/install-dependencies.sh

@@ -1,62 +1,16 @@
 #!/usr/bin/env bash
 
-ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE:-$0}")"; pwd)
-
-echo "PYTHON is $PYTHON"
-
-platform="unknown"
-unamestr="$(uname)"
-if [[ "$unamestr" == "Linux" ]]; then
-  echo "Platform is linux."
-  platform="linux"
-elif [[ "$unamestr" == "Darwin" ]]; then
-  echo "Platform is macosx."
-  platform="macosx"
-else
-  echo "Unrecognized platform."
-  exit 1
-fi
-
-if [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "linux" ]]; then
-  # Install miniconda.
+if [[ "$PYTHON" == "2.7" ]]; then
   wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh -nv
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q pandas==0.22 feather-format lxml openpyxl xlrd
-  # Install ray from its latest wheels
-  pip install -q -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp27-cp27mu-manylinux1_x86_64.whl
-elif [[ "$PYTHON" == "3.6" ]] && [[ "$platform" == "linux" ]]; then
-  # Install miniconda.
-  wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh -nv
-  bash miniconda.sh -b -p $HOME/miniconda
-  export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q pandas==0.22 feather-format lxml openpyxl xlrd
-  # Install ray from its latest wheels
-  pip install -q -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp36-cp36m-manylinux1_x86_64.whl
-elif [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "macosx" ]]; then
-  # Install miniconda.
-  wget https://repo.continuum.io/miniconda/Miniconda2-latest-MacOSX-x86_64.sh -O miniconda.sh -nv
-  bash miniconda.sh -b -p $HOME/miniconda
-  export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q pandas==0.22 feather-format lxml openpyxl xlrd
-  # Install ray from its latest wheels
-  pip install -q -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp27-cp27m-macosx_10_6_intel.whl
-elif [[ "$PYTHON" == "3.6" ]] && [[ "$platform" == "macosx" ]]; then
-  # Install miniconda.
-  wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh -nv
-  bash miniconda.sh -b -p $HOME/miniconda
-  export PATH="$HOME/miniconda/bin:$PATH"
-  pip install -q pandas==0.22 feather-format lxml openpyxl xlrd
-  # Install ray from its latest wheels
-  pip install -q -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp36-cp36m-macosx_10_6_intel.whl
-elif [[ "$LINT" == "1" ]]; then
-  # Install miniconda.
+
+elif [[ "$PYTHON" == "3.6" ]] || [[ "$LINT" == "1" ]]; then
   wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh -nv
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
-  # Install Python linting tools.
-  pip install -q flake8 flake8-comprehensions yapf
-else
-  echo "Unrecognized environment."
-  exit 1
+  conda install -y python==3.6.5
 fi
+
+pip install -r requirements.txt
+pip install -q pytest flake8 flake8-comprehensions yapf feather-format lxml openpyxl xlrd numpy

modin/pandas/concat.py

@@ -117,8 +117,8 @@ def name_incrementer(i):
     # Put all of the DataFrames into Ray format
     # TODO just partition the DataFrames instead of building a new Ray DF.
     objs = [
-        DataFrame(obj)
-        if isinstance(obj, (pandas.DataFrame, pandas.Series)) else obj
+        DataFrame(obj) if isinstance(obj, (pandas.DataFrame,
+                                           pandas.Series)) else obj
         for obj in objs
     ]
 

modin/pandas/dataframe.py

@@ -375,8 +375,9 @@ def col_dots_builder(full_front, full_back):
             col_dots = pandas.Series(["..." for _ in range(len(full_front))])
             col_dots.index = index_of_head
             col_dots.name = "..."
-            return pandas.concat(
-                [full_front, col_dots, full_back], axis=1, copy=False)
+            return pandas.concat([full_front, col_dots, full_back],
+                                 axis=1,
+                                 copy=False)
 
         # If we don't exceed the maximum number of values on either dimension
         if len(self.index) <= 60 and len(self.columns) <= 20:
@@ -524,8 +525,8 @@ def _arithmetic_helper(self, remote_func, axis, level=None):
 
         oid_series = ray.get(
             _map_partitions(
-                remote_func, self._col_partitions
-                if axis == 0 else self._row_partitions))
+                remote_func,
+                self._col_partitions if axis == 0 else self._row_partitions))
 
         if axis == 0:
             # We use the index to get the internal index.
@@ -538,8 +539,9 @@ def _arithmetic_helper(self, remote_func, axis, level=None):
                     df.index = \
                         this_partition[this_partition.isin(df.index)].index
 
-            result_series = pandas.concat(
-                [obj[0] for obj in oid_series], axis=0, copy=False)
+            result_series = pandas.concat([obj[0] for obj in oid_series],
+                                          axis=0,
+                                          copy=False)
         else:
             result_series = pandas.concat(oid_series, axis=0, copy=False)
             result_series.index = self.index
@@ -1507,9 +1509,9 @@ def astype(self, dtype, copy=True, errors='raise', **kwargs):
                 raise KeyError("Only a column name can be used for the key in"
                                "a dtype mappings argument.")
             columns = list(dtype.keys())
-            col_idx = [(self.columns.get_loc(columns[i]), columns[i]) if
-                       columns[i] in self.columns else (columns[i], columns[i])
-                       for i in range(len(columns))]
+            col_idx = [(self.columns.get_loc(columns[i]),
+                        columns[i]) if columns[i] in self.columns else
+                       (columns[i], columns[i]) for i in range(len(columns))]
             new_dict = {}
             for idx, key in col_idx:
                 new_dict[idx] = dtype[key]
@@ -3667,8 +3669,9 @@ def check_bad_dtype(t):
                 if next((True for t in self.dtypes if check_bad_dtype(t)),
                         False):
                     dtype = next(t for t in self.dtypes if check_bad_dtype(t))
-                    raise ValueError("Cannot compare type '{}' with type '{}'"
-                                     .format(type(dtype), float))
+                    raise ValueError(
+                        "Cannot compare type '{}' with type '{}'".format(
+                            type(dtype), float))
         else:
             # Normally pandas returns this near the end of the quantile, but we
             # can't afford the overhead of running the entire operation before
@@ -5896,8 +5899,8 @@ def __neg__(self):
         for t in self.dtypes:
             if not (is_bool_dtype(t) or is_numeric_dtype(t)
                     or is_timedelta64_dtype(t)):
-                raise TypeError("Unary negative expects numeric dtype, not {}"
-                                .format(t))
+                raise TypeError(
+                    "Unary negative expects numeric dtype, not {}".format(t))
 
         new_block_partitions = np.array([
             _map_partitions(lambda df: df.__neg__(), block)

modin/pandas/groupby.py

@@ -568,8 +568,8 @@ def _apply_df_function(self, f, concat_axis=None):
             new_df._block_partitions = np.array([
                 _reindex_helper._submit(
                     args=tuple([
-                        new_df.columns, self._columns, 0,
-                        new_df._block_partitions.shape[1]
+                        new_df.columns, self._columns, 0, new_df.
+                        _block_partitions.shape[1]
                     ] + block.tolist()),
                     num_return_vals=new_df._block_partitions.shape[1])
                 for block in new_df._block_partitions

modin/pandas/index_metadata.py

@@ -199,8 +199,8 @@ def groupby(self,
         return assignments_df
 
     def partition_series(self, partition):
-        return self[self._coord_df['partition'] == partition,
-                    'index_within_partition']
+        return self[self._coord_df['partition'] ==
+                    partition, 'index_within_partition']
 
     def __len__(self):
         return int(sum(self._lengths))

modin/pandas/indexing.py

@@ -315,8 +315,9 @@ def _write_items(self, row_lookup, col_lookup, item):
                 row_idx = row_data['index_within_partition']
                 col_idx = col_data['index_within_partition']
 
-                item_to_write = item[row_item_index:row_item_index + row_len,
-                                     col_item_index:col_item_index + col_len]
+                item_to_write = item[row_item_index:row_item_index +
+                                     row_len, col_item_index:col_item_index +
+                                     col_len]
 
                 result_oid = writer.remote(block_oid, row_idx, col_idx,
                                            item_to_write)
@@ -396,15 +397,15 @@ def _enlarge_axis(self, locator, axis):
         ]])
         nan_blks = nan_blks.T if not row_based_bool else nan_blks
 
-        self.block_oids = np.concatenate(
-            [self.block_oids, nan_blks], axis=0 if row_based_bool else 1)
+        self.block_oids = np.concatenate([self.block_oids, nan_blks],
+                                         axis=0 if row_based_bool else 1)
 
         # 3. Prepare metadata to return
         nan_coord_df = pandas.DataFrame(data=[
             {
                 '': name,
-                'partition': blk_part_n_row
-                if row_based_bool else blk_part_n_col,
+                'partition':
+                blk_part_n_row if row_based_bool else blk_part_n_col,
                 'index_within_partition': i
             } for name, i in zip(nan_labels, np.arange(num_nan_labels))
         ]).set_index('')

modin/pandas/test/test_dataframe.py

@@ -1870,13 +1870,12 @@ def test_fillna_dataframe():
     ray_df = pd.DataFrame(frame_data, index=list('VWXYZ'))
 
     # df2 may have different index and columns
-    df2 = pandas.DataFrame(
-        {
-            'a': [np.nan, 10, 20, 30, 40],
-            'b': [50, 60, 70, 80, 90],
-            'foo': ['bar'] * 5
-        },
-        index=list('VWXuZ'))
+    df2 = pandas.DataFrame({
+        'a': [np.nan, 10, 20, 30, 40],
+        'b': [50, 60, 70, 80, 90],
+        'foo': ['bar'] * 5
+    },
+                           index=list('VWXuZ'))
 
     # only those columns and indices which are shared get filled
     assert ray_df_equals_pandas(ray_df.fillna(df2), df.fillna(df2))
@@ -2567,7 +2566,8 @@ def test_rename_sanity():
         }).index)
 
     tm.assert_index_equal(
-        ray_df.rename(index=str.upper).index, df.rename(index=str.upper).index)
+        ray_df.rename(index=str.upper).index,
+        df.rename(index=str.upper).index)
 
     # have to pass something
     pytest.raises(TypeError, ray_df.rename)
@@ -3145,8 +3145,8 @@ def test_update():
     df = pd.DataFrame([[1.5, np.nan, 3.], [1.5, np.nan, 3.], [1.5, np.nan, 3],
                        [1.5, np.nan, 3]])
 
-    other = pd.DataFrame(
-        [[3.6, 2., np.nan], [np.nan, np.nan, 7]], index=[1, 3])
+    other = pd.DataFrame([[3.6, 2., np.nan], [np.nan, np.nan, 7]],
+                         index=[1, 3])
 
     df.update(other)
 

modin/pandas/utils.py

@@ -303,9 +303,8 @@ def _repartition_coord_df(old_coord_df, npartition):
         passed in.
     """
     length = len(old_coord_df)
-    chunksize = (len(old_coord_df) // npartition
-                 if len(old_coord_df) % npartition == 0 else
-                 len(old_coord_df) // npartition + 1)
+    chunksize = (len(old_coord_df) // npartition if len(old_coord_df) %
+                 npartition == 0 else len(old_coord_df) // npartition + 1)
 
     # genereate array([0, 0, 0, 1, 1, 1, 2])
     partitions = np.repeat(np.arange(npartition), chunksize)[:length]
@@ -314,12 +313,11 @@ def _repartition_coord_df(old_coord_df, npartition):
     final_n_partition = np.max(partitions)
     idx_in_part = np.tile(np.arange(chunksize), final_n_partition + 1)[:length]
 
-    final_df = pandas.DataFrame(
-        {
-            'partition': partitions,
-            'index_within_partition': idx_in_part
-        },
-        index=old_coord_df.index)
+    final_df = pandas.DataFrame({
+        'partition': partitions,
+        'index_within_partition': idx_in_part
+    },
+                                index=old_coord_df.index)
 
     return final_df
 
@@ -767,8 +765,8 @@ def apply_suffix(s):
             block_df_oid = blk_partitions[row_idx, col_idx]
             block_df = ray.get(block_df_oid)
             chunk = block_df.iloc[row_df[apply_suffix(
-                'index_within_partition')], col_df[apply_suffix(
-                    'index_within_partition')]]
+                'index_within_partition'
+            )], col_df[apply_suffix('index_within_partition')]]
             this_column.append(chunk)
         df_columns.append(pandas.concat(this_column, axis=1))
     final_df = pandas.concat(df_columns)

requirements.txt

@@ -1,11 +1,2 @@
 pandas==0.22
-# Install Ray from latest wheels
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp27-cp27mu-manylinux1_x86_64.whl ; sys_platform == "linux2" and python_version == "2.7"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp33-cp33m-manylinux1_x86_64.whl ; sys_platform == "linux" and python_version == "3.3"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp34-cp34m-manylinux1_x86_64.whl ; sys_platform == "linux" and python_version == "3.4"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp35-cp35m-manylinux1_x86_64.whl ; sys_platform == "linux" and python_version == "3.5"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp36-cp36m-manylinux1_x86_64.whl ; sys_platform == "linux" and python_version == "3.6"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp27-cp27m-macosx_10_6_intel.whl ; sys_platform == "darwin" and python_version == "2.7"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp34-cp34m-macosx_10_6_intel.whl ; sys_platform == "darwin" and python_version == "3.4"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp35-cp35m-macosx_10_6_intel.whl ; sys_platform == "darwin" and python_version == "3.5"
-https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp36-cp36m-macosx_10_6_intel.whl ; sys_platform == "darwin" and python_version == "3.6"
+ray==0.5.2