def get_model_data(model_type, model_name, compression_options=None):
from preconvert.output import json
if model_name in data_cache:
return data_cache[model_type + "_" + model_name]
""" Gets the model data """
try:
data = pkgutil.get_data("data", "shrynk/{}_{}.jsonl.gzip".format(model_type, model_name))
data = [
json.loads(line) for line in decompress(data).decode("utf8").split("\n") if line.strip()
]
# print("from package")
except FileNotFoundError:
try:
with open(shrynk_path("{}_{}.jsonl".format(model_type, model_name))) as f:
data = [json.loads(x) for x in f.read().split("\n") if x]
except FileNotFoundError:
data = []
if compression_options is not None:
known_kwargs = set([json.dumps(x) for x in compression_options])
for x in data:
x["bench"] = [y for y in x["bench"] if y["kwargs"] in known_kwargs]
# print("filtered compressions")
data_cache[model_type + "_" + model_name] = data
return data
def iread(fn):
from preconvert.output import json
if not isinstance(fn, str):
raise TypeError("Cannot iteratively read compressed file now")
with open(fn) as f:
for i, line in enumerate(f):
try:
yield json.loads(line)
except Exception as e:
msg = "JSON-L parsing error in line number {} in the jsonl file".format(
i)
raise Exception(msg, line)
def predict(self, features):
from preconvert.output import json
if isinstance(features, pd.DataFrame):
features = self.get_features(features)
if isinstance(features, dict):
features = pd.DataFrame([features])
warnings.filterwarnings(module='sklearn*',
action='ignore',
category=DeprecationWarning)
pred = self.clf.predict(features.fillna(-100))[0]
if not isinstance(pred, str):
pred = pred[0]
return json.loads(pred)
def get_benchmark_html(df, fname):
features = pdc.get_features(df)
bench_res = None
save = False
if IN_PRODUCTION:
blob = get_blob(features)
if blob.exists():
results = json.loads(blob.download_as_string())
bench_res = results["bench"]
else:
results = pdc.run_benchmarks(df,
save=False,
ignore_seen=False,
timeout=False)[0]
# make a copy not to pop kwargs from results object which will be saved
bench_res = deepcopy(results)["bench"]
save = True
else:
bench_res = pdc.run_benchmarks(df,
save=False,
ignore_seen=False,
timeout=False)[0]["bench"]
kwargs = [x.pop("kwargs") for x in bench_res]
bench_res = pd.DataFrame(bench_res, index=kwargs)
inferred = pdc.infer(features)
z_name = "z {}".format(tuple(weights))
bench_res[z_name] = (scale(bench_res) * weights).sum(axis=1)
bench_res = bench_res.round(5).sort_values(z_name)
bench_res = bench_res[[z_name, "size", "write_time", "read_time"]]
y = json.dumps(inferred)
res_index = [i + 1 for i, x in enumerate(bench_res.index) if x == y] + [-1]
if save:
ip = request.environ.get("HTTP_X_FORWARDED_FOR", "")
ip = ip.split(",")[0]
results["web"] = {
"utctime": datetime.utcnow().isoformat(),
"ip": ip,
"predicted": inferred,
"res_index": res_index[0], # 1 is 1st, 2 is 2nd
"filename": fname,
"weights": weights.tolist(),
}
blob.upload_from_string(json.dumps(results))
print("saved blob")
bench_res.index = [
" ".join(["{}={!r}".format(k, v) for k, v in json.loads(x).items()])
for x in bench_res.index
]
learning = "none" if res_index and res_index[0] == 1 else "inherit"
nth = {
1: "1st",
2: "2nd",
3: "3rd",
-1: "999"
}.get(res_index[0],
str(res_index[0]) + "th")
# upload(features, "{}-{}".format(file.filename, time.time()))
features = {
k.replace("quantile_proportion", "quantile"):
round(v, 3) if isinstance(v, float) else v
for k, v in features.items()
}
return str(
Markup(
'<center> <h5 class="tagline"> Results: </h5></center>' +
'<div class="container" style="margin-top: 2rem"><div class="row">'
+
'<div class="col l10 offset-l2" style="padding-bottom: 2rem; padding-top: 1rem;">The data was featurized, and a prediction was made. Then, all the compressions were ran for this file so we can see if the prediction was correct (the ground truth).</div>'
+ '<div class="col s12 m6 l3 offset-l2">' + "<b>Filename: </b>" +
fname + "<br><b>Features: </b>" + '<code class="codes">' +
json.dumps(features, indent=4) + "</code>" + '</div>' +
'<div class="col s12 m6 l3 offset-l3">' +
"<br><center style='line-height: 3'><b>Predicted: </b><br>"
# just using features here instead of data to be faster
+ " ".join(["{}={!r}".format(k, v) for k, v in inferred.items()]) +
"<br><b>Result:</b><br><span class='result {}'>{}</span> / {}<br><div style='display: {}'><span style='color: #ee6e73'>Wrong!</span> We will learn from this...</div>"
.format(nth[-2:], nth, bench_res.shape[0], learning) +
"</center></div></div>" +
"<center><h4>Ground truth</h4><div class='show-on-small hide-on-med-and-up' style='padding: 0.5rem; color: grey'> -- scroll -> </center>"
+ replacenth(
format_res(bench_res, tuple(weights), fname),
"<tr ",
'<tr class="resultinv {}" '.format(nth[-2:]),
int(nth[:-2]),
)))
