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Fix api changes for v12 for the strings blog #270

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Fix api changes for v12 for the strings blog #270

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2bfb6e1
Merge pull request #250 from rapidsai/taureandyernv-colab-bulletin
taureandyernv Nov 28, 2019
3bbf5a9
Merge pull request #251 from rapidsai/taureandyernv-colab-bulletin
taureandyernv Nov 28, 2019
2df0b26
Merge pull request #252 from rapidsai/taureandyernv-colab-bulletin
taureandyernv Nov 28, 2019
7c3ceb7
Merge pull request #254 from rapidsai/taureandyernv-colab-bulletin
taureandyernv Nov 28, 2019
6458bb1
fix api call for v12
VibhuJawa Jan 23, 2020
70732ed
fix typos
VibhuJawa Jan 23, 2020
0c5f943
1 last typo
VibhuJawa Jan 23, 2020
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26 changes: 16 additions & 10 deletions blog_notebooks/nlp/show_me_the_word_count_gutenberg/show_me_the_word_count_gutenberg.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -191,7 +191,13 @@
"df = df.drop(labels=['label'])\n",
"\n",
"print(\"Number of lines in the DF = {:,}\".format(len(df)))\n",
"df.head(5).to_pandas()"
"df.head(5).to_pandas()\n",
"\n",
"## keeping only 90% because of the books\n",
"## because of temporary 1.5x overhead \n",
"## introduced in 0.12\n",
"## this will away with 0.13\n",
"df = df.head(int(len(df)*0.90))"
]
},
{
Expand Down Expand Up @@ -328,7 +334,7 @@
"metadata": {},
"outputs": [],
"source": [
"text_col_sample['text_clean'] = nvtext.replace_tokens(text_col_sample['text_clean'].data, STOPWORDS, ' ')\n",
"text_col_sample['text_clean'] = nvtext.replace_tokens(text_col_sample['text_clean']._column.nvstrings, STOPWORDS, ' ')\n",
"text_col_sample['text_clean'].to_pandas()"
]
},
Expand Down Expand Up @@ -394,7 +400,7 @@
" \n",
" # remove stopwords\n",
" stopwords_gpu = nvstrings.to_device(stopwords)\n",
" input_strs = nvtext.replace_tokens(input_strs.data, stopwords_gpu, ' ')\n",
" input_strs = nvtext.replace_tokens(input_strs._column.nvstrings, stopwords_gpu, ' ')\n",
" input_strs = cudf.Series(input_strs)\n",
" \n",
" # replace multiple spaces with single one and strip leading/trailing spaces\n",
Expand Down Expand Up @@ -464,7 +470,7 @@
" df = cudf.DataFrame()\n",
" # tokenize sentences into a string using nvtext.tokenize()\n",
" # it into a single tall data-frame\n",
" df['string'] = nvtext.tokenize(str_col.data)\n",
" df['string'] = nvtext.tokenize(str_col._column.nvstrings)\n",
" \n",
" # Using Group by to do a value count for string columns\n",
" # This will be natively supported soon\n",
Expand Down Expand Up @@ -634,8 +640,8 @@
" \"\"\"\n",
" import rmm\n",
" \n",
" cat = nvcategory.from_strings(str_s.data).set_keys(keys)\n",
" device_array = rmm.device_array(str_s.data.size(), dtype=np.int32) \n",
" cat = nvcategory.from_strings(str_s._column.nvstrings).set_keys(keys)\n",
" device_array = rmm.device_array(str_s._column.nvstrings.size(), dtype=np.int32) \n",
" cat.values(devptr=device_array.device_ctypes_pointer.value)\n",
" \n",
" return cudf.Series(device_array)\n",
Expand Down Expand Up @@ -674,7 +680,7 @@
"%%time\n",
"# keep only top 20k words in the dataset\n",
"th = 20_000\n",
"keys = count_df['string'][:th].data\n",
"keys = count_df['string'][:th]._column.nvstrings\n",
"encoded_wc_ls = []\n",
"\n",
"for auth_wc_df in author_wc_ls:\n",
Expand Down Expand Up @@ -805,8 +811,8 @@
" count_sr = encoded_wc_df['counts']\n",
" token_id_sr = encoded_wc_df['encoded_str_id']\n",
" \n",
" count_ar = count_sr.data.to_gpu_array()\n",
" token_id_ar = token_id_sr.data.to_gpu_array()\n",
" count_ar = count_sr.to_gpu_array()\n",
" token_id_ar = token_id_sr.to_gpu_array()\n",
" author_ar = count_dary[author_id]\n",
" \n",
" # See https://numba.pydata.org/numba-doc/0.13/CUDAJit.html\n",
Expand Down Expand Up @@ -1015,7 +1021,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
}
},
"nbformat": 4,
Expand Down